Compression encoder, compression encoding method and program

ABSTRACT

A DWT unit applies wavelet transform to an input image signal to output transform coefficients, and on those transform coefficients, a quantization unit performs quantization, sorting and bit shifting with a quantization step size determined by target image quality in an image-quality control unit as well as sorting and bit shifting based on information on a region of interest specified by a ROI unit. A coefficient bit modeling unit applies bit modeling on transform coefficients outputted from the quantization unit on a bit-plane-by-bit-plane basis, and an arithmetic coding unit applies arithmetic coding on coded data inputted from the coefficient bit modeling unit. Then, a rate control unit controls the rate of coded data inputted from the arithmetic coding unit.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a compression encoder and compression encoding method for use in image compression and expansion technology.

2. Description of the Background Art

As a next-generation high-efficiency coding standard for image data, the International Organization for Standardization (ISO) and the International Telecommunication Union-Telecommunication Standardization Sector (ITU-T) have been developing the Joint Photographic Experts Group 2000 (JPEG2000) standard. The JPEG2000 standard provides functions superior to the Joint Photographic Experts Group (JPEG) standard which is currently in the mainstream, and features the adoption of discrete wavelet transform (DWT) for orthogonal transformation and of a technique called “Embedded Block Coding with Optimized Truncation” (EBCOT) which performs bit-plane coding, for entropy coding.

FIG. 25 is a functional block diagram showing a general configuration of a compression encoder 100 for image compressing and coding based on the JPEG2000 standard. Hereinbelow, the procedure of compression and coding according to the JPEG2000 standard is generally described with reference to FIG. 25.

An image signal inputted to the compression encoder 100 is DC level shifted in a DC level shift unit 102 as needed, and outputted to a color-space conversion unit 103. The color-space conversion unit 103 converts the color space of a signal inputted from the DC level shift unit 102. For example, an RGB signal inputted to the color-space conversion unit 103 is converted into a YcbCr signal (a signal consisting of a luminance signal Y and color-difference signals Cb and Cr).

Then, a tiling unit 104 divides an image signal inputted from the color-space conversion unit 103 into a plurality of rectangular regional components called “tiles”, and outputs those components to a DWT unit 105. The DWT unit 105 performs integer or real-number DWT on each tile of an image signal inputted from the tiling unit 104 and outputs resultant transform coefficients. In DWT, a one-dimensional (1-D) filter which divides a two-dimensional (2-D) image signal into high-pass (high-frequency) and low-pass (low-frequency) components, is applied in vertical and horizontal directions in this order. In the fundamentals of the JPEG2000 standard, an octave band splitting method is adopted in which only those bandpass components (subbands) which are divided into the low frequency side in both the vertical and horizontal directions are recursively divided into further subbands. The number of recursive divisions is called the decomposition level.

FIG. 26 is a schematic view showing a 2-D image 120 subjected to the DWT with the third decomposition level by the octave band splitting method. At the first decomposition level, the 2-D image 120 is divided into four subbands HH1, HL1, LH1 and LL1 (not shown) by sequential application of the aforementioned 1-D filter in the vertical and horizontal directions. Here, “H” and “L” stand for high- and low-pass components, respectively. For example, HL1 is the subband consisting of a horizontally high-pass component H and a vertically low-pass component L of the first decomposition level. To generalize the notation, “XYn” (X and Y are either H or L; n is an integer of 1 or more) represents a subband consisting of a horizontal component X and a vertical component Y of the n-th decomposition level.

At the second decomposition level, the low-pass component LL1 is divided into subbands HH2, HL2, LH2 and LL2 (not shown). Further, at the third decomposition level, the low-pass component LL2 is divided into further subbands HH3, HL3, LH3 and LL3. An arrangement of the resultant subbands HH1, HL1, LH1, HH2, HL2, LH2, HH3, HL3, LH3 and LL3 is shown in FIG. 26. While FIG. 26 shows an example of third-order decomposition, the JPEG2000 standard generally adopts approximately third- to eighth-order decomposition.

A quantization unit 106 has the function of performing scalar quantization on transform coefficients outputted from the DWT unit 105 as needed. The quantization unit 106 also has the function of performing a bit-shift operation in which higher priority is given to the image quality of a region of interest (ROI) which is specified by a ROI unit 107. Now, in reversible (lossless) transformation, scalar quantization is not performed in the quantization unit 106. The JPEG2000 standard provides two kinds of quantization means: the scalar quantization in the quantization unit 106 and post-quantization (truncation) which will be described later.

A representative method of utilizing ROI is the Max-shift method which is specified as an optional function of JPEG2000.

The Max-shift method is to arbitrarily specify a ROI and compress the ROI with high image quality while compressing a non-ROI with low image quality. More specifically, an original image is first subjected to wavelet transform to obtain distributions of wavelet coefficients, and a value Vm of the highest wavelet coefficient in a coefficient distribution corresponding to the non-ROI among the distributions of wavelet coefficients. Then, the number of bits “S” which satisfies S>=max (Vm) is obtained, to shift wavelet coefficients for only the ROI by S bits so as to be incremented. For instance, when the value Vm is “255” in decimal notation (i.e., “11111111” in binary notation), S is 8. When the value Vm is “128” in decimal notation (i.e., “10000000” in binary notation), S is also 8. Accordingly, in either case, the wavelet coefficients for the ROI are shifted by S=8 bits so as to be incremented. It is therefore possible to set the ROI to have a lower compression ratio than the non-ROI, allowing high-quality compressed data to be acquired for the ROI.

Then, transform coefficients outputted from the quantization unit 106 are, according to the aforementioned EBCOT, entropy coded on a block-by-block basis in a coefficient bit modeling unit 108 and an arithmetic coding unit 109, and are rate controlled in a rate control unit 110. More specifically, the coefficient bit modeling unit 108 divides each subband of input transform coefficients into regions called “code blocks” of, for example, approximately size 16×16, 32×32, or 64×64 and further decomposes each code block into a plurality of bit planes each constituting a two-dimensional array of respective one bits of the transform coefficients.

FIG. 27 is a schematic view showing the 2-D image 120 decomposed into a plurality of code blocks 121. FIG. 28 is a schematic view showing n bit planes 122 ₀ through 122 _(n−1) (n is a natural number) constituting these code blocks 121. As shown in FIG. 28, decomposition is performed such that, where a binary value 123 representing one transform coefficient in a code block 121 is “011 . . . 0”, then bits constituting this binary value 123 belong respectively to the bit planes 122 _(n−1), 122 _(n−2), 122 _(n−3), . . . , and 122 ₀. In the figure, the bit plane 122 _(n−1) represents the most-significant bit plane consisting only of the most-significant bits (MSB) of the transform coefficients, and the bit plane 122 ₀ represents the least-significant bit plane consisting only of the least-significant bits (LSB) of the transform coefficients.

Then, the coefficient bit modeling unit 108 judges the context of each bit in each bit plane 122 _(k) (k=0 to n−1), and as shown in FIG. 29, decomposes the bit plane 122 _(k) according to the significance of each bit judgment result, into three types of coding passes: a significance propagation (SIG) pass, a magnitude refinement (MR) pass, and a cleanup (CL) pass. The context judgment algorithm for each coding pass is determined by the EBCOT. According to the algorithm, the state of being “significant” means that a coefficient concerned has already been found not to be zero in previous coding, and the state of being “not significant” means that the value of a coefficient concerned is or possibly zero.

The coefficient bit modeling unit 108 performs bit-plane coding with three types of coding passes: the SIG pass (coding pass for insignificant coefficients with significant neighbors), the MR pass (coding pass for significant coefficients), and the CL pass (coding pass for the remaining coefficients which belongs to neither the SIG nor MR pass). The bit-plane coding is performed, starting from the most-significant to the least-significant bit plane, by scanning each bit plane in four bits at a time and determining whether there exist significant coefficients. The number of bit planes consisting only of insignificant coefficients (0 bits) is recorded in a packet header, and actual coding starts from a bit plane where a significant coefficient first appears. The bit plane from which coding starts is coded in only the CL pass, and lower-order bit planes than that bit plane are sequentially coded in the above three types of coding passes.

FIG. 30 shows the rate-distortion (R-D) curve representing the relationship between rate (R) and distortion (D). In this R-D curve, R₁ represents the rate before bit-plane coding, R₂ the rate after bit-plane coding, D₁ the distortion before bit-plane coding, and D₂ the distortion after bit-plane coding. In the figure, A, B and C are labels representing the above coding passes. For efficient coding, as a route from the starting point P₁ (R₁, D₁) to the end point P₂ (R₂, D₂), the route A-B-C of a concave curve is more desirable than the route C-B-A of a convex curve. In order to achieve such a concave curve, it is known that coding should start from the MSB plane to the LSB plane.

Then, the arithmetic coding unit 109, using an MQ coder and according to the result of context judgment, performs arithmetic coding of a coefficient sequence provided from the coefficient bit modeling unit 108 on a coding-pass-by-coding-pass basis. This arithmetic coding unit 109 also has a mode of performing bypass processing in which a part of the coefficient sequence inputted from the coefficient bit modeling unit 108 is not arithmetically coded.

Then, the rate control unit 110 performs post-quantization for truncation of lower-order bit planes of a code sequence outputted from the arithmetic coding unit 109, thereby to control a final rate. A bit-stream generation unit 111 generates a bit stream by multiplexing a code sequence outputted from the rate control unit 110 and attached information (header information, layer structure, scalability information, quantization table, etc.) and outputs it as a compressed image.

The compression encoder with the aforementioned configuration adopts, as a method for compressing the amount of image data, for example a technique called rate-distortion (R-D) optimization utilizing the rate control method employed in the rate control unit 110 (cf. David S. Taubman and Michael W. Marcellin, “JPEG2000 Image Compression Fundamentals, Standards and Practice,” Kluwer Academic Publishers, which is hereinafter referred to as the “first non-patent literature”).

This method, however, arises problems of: (1) requiring calculating the amount of distortion for each rate in each coding pass, and the optimal solution for a certain coding rate has to be estimated, resulting in a great number of operations and low immediacy; and (2) requiring providing a memory for storing the amount of distortion calculated in each coding pass.

Particularly for quantization and rate control which directly affects performance characteristics of a compression encoder, there is a demand for a method for achieving efficient processing at high speeds while maintaining high image quality.

SUMMARY OF THE INVENTION

An object of the present invention is to compress and code image data at high speeds with minimal operations. Another object of the invention is to obtain a compressed image that meets a target rate while maintaining image quality.

According to a first aspect of the present invention, a compression encoder for compression and coding of an image signal comprises a wavelet transformer for recursively dividing an image signal into high- and low-pass components by wavelet transform and generating and outputting transform coefficients in a plurality of bandpass components, an image-quality controller for determining a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient, a region-of-interest setting unit for setting a region of interest in the image signal, a quantizer for quantizing and sorting the transform coefficients with the quantization step size as well as sorting and bit shifting the transform coefficients on the basis of information on setting the region of interest, an entropy coder for entropy coding the transform coefficients inputted from the quantizer, and a rate controller for controlling a rate by truncating part of coded data outputted from the entropy coder so that a total capacity of the coded data meets a target rate.

This allows a compressed image to be readily generated that meets a target rate while maintaining image quality of a region of interest. Further, processing can be achieved by the Max-shift method provided for a conventional compression encoder which is specified as an optional function of JPEG2000.

According to a second aspect of the invention, a compression encoder for compression and coding of an image signal comprises a wavelet transformer for recursively dividing an image signal into high- and low-pass components by wavelet transform and generating and outputting transform coefficients in a plurality of bandpass components, a region-of-interest setting unit for setting a region of interest in the image signal, a quantizer for quantizing the transform coefficients, an entropy coder for entropy coding the transform coefficients inputted from the quantizer, and a rate controller for controlling a rate by sorting and bit shifting coded data outputted from the entropy coder on the basis of information on setting the region of interest to generate a code sequence and then by truncating part of the code sequence so that a total capacity of the coded data meets a target rate.

This allows a compressed image to be readily generated that meets a target rate while maintaining image quality of a region of interest. Further, processing can be achieved by a conventional compression encoder not provided with the Max-shift method which is specified as an optional function of JPEG2000.

Preferably, the compression encoder according to the second aspect further comprises an image-quality controller for determining a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient. The quantizer quantizes the transform coefficients with the quantization step size. The rate controller sorts the coded data with the quantization step size.

This achieves high speed processing with minimal operations as compared with conventional techniques which require processes for estimating an optimal solution while controlling a data compression ratio in quantization according to target image quality.

More preferably, in the compression encoder according to the second aspect, the region of interest includes a plurality of regions of interest. The region-of-interest setting unit assigns a priority to each of the plurality of regions of interest specified. The amount of shift in the bit shifting on the basis of the information on setting the plurality of regions of interest is determined by the priority.

Each of the plurality of regions of interest is assigned a priority to generate a compressed image that meets a target rate while maintaining image quality of a desired region.

More preferably, in the compression encoder according to the first aspect, the rate controller determines a target to be truncated in the rate control on a bit-plane-by-bit-plane basis.

This achieves minute and efficient rate control on a bit-plane-by-bit-plane basis according to target image quality.

More preferably, in the compression encoder according to the first aspect, the rate controller determines a target to be truncated in the rate control on a pass-by-pass basis.

This achieves minute and efficient rate control on a pass-by-pass basis according to target image quality.

More preferably, in the compression encoder according to the first aspect, the image-quality controller determines the quantization step size applying weighting in consideration of human visual characteristics by dividing the quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.

This allows a compressed image to be generated with high display image quality in consideration of the human visual characteristics.

More preferably, in the compression encoder according to the first aspect, the image-quality controller, when the quantization step size is less than a predetermined value, multiplies the quantization step size by powers of 2 so that the quantization step size is not less than the predetermined value.

This provides a device for performing quantization efficiently according to target image quality.

More preferably, in the compression encoder according to the first aspect, the rate controller, when the quantization step size is a value obtained by multiplication by powers of 2 in the image-quality controller, bit shifts the transform coefficients, by the number of bits corresponding to an exponent of the powers of 2 in performing sorting with the quantization step size.

This allows a compressed image to be generated efficiently while maintaining image quality that meets a target rate.

These and other objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description of the present invention when taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a general configuration of a compression encoder according to a first preferred embodiment of the present invention;

FIGS. 2 to 4 give numerical tables of energy weighting factors;

FIG. 5 shows a wavelet plane of a luminance signal;

FIG. 6 shows a wavelet plane of color difference signals in YUV 422 format;

FIG. 7 shows a wavelet plane of color difference signals in YUV 420 format;

FIG. 8 shows an example of an original image;

FIG. 9A shows a single mask region specified in the original image shown in FIG. 8, and FIG. 9B shows a plurality of mask regions specified in the original image shown in FIG. 8;

FIGS. 10A and 10B respectively show the mask regions shown in FIGS. 9A and 9B expanded into a wavelet plane;

FIG. 11 shows the correspondence of mask regions between the low- and high-pass sides and input side in an inverse wavelet 5/3-tap filter;

FIG. 12 shows the correspondence of mask regions between the low- and high-pass sides and input side in an inverse wavelet 9/7-tap filter;

FIG. 13 shows bit shifting of a code sequence;

FIG. 14 shows sorting of a code sequence;

FIG. 15 shows sorting and bit shifting of a code sequence in YUV format;

FIG. 16 shows sorting based on ROI information;

FIG. 17 shows bit shifting based on the ROI information;

FIG. 18 shows a general configuration of a compression encoder according to a second preferred embodiment of the invention;

FIG. 19 shows an example of specifying ROIs in image data;

FIG. 20 shows the ROIs shown in FIG. 19 as expanded into wavelet plane;

FIG. 21 shows sorting and bit shifting of a code sequence based on information on setting the ROIs shown in FIG. 19;

FIG. 22 shows tiling of image data;

FIG. 23 shows sorting and bit shifting of a tiled code sequence in YUV format;

FIG. 24 shows sorting and bit shifting of a code sequence based on ROI information;

FIG. 25 shows a general configuration of a compression encoder according to the JPEG2000 standard;

FIG. 26 is a schematic view showing a two-dimensional (2-D) image divided into subbands according to an octave band splitting method;

FIG. 27 is a schematic view showing a 2-D image decomposed into a plurality of code blocks;

FIG. 28 is a schematic view showing a plurality of bit planes constituting a code block;

FIG. 29 is a schematic view showing three types of coding passes; and

FIG. 30 shows an R-D curve representing the relationship between rate and distortion.

DESCRIPTION OF THE PREFERRED EMBODIMENTS First Preferred Embodiment

Compression Encoder

FIG. 1 is a functional block diagram showing a general configuration of a compression encoder 1 according to a first preferred embodiment of the present invention. After general description of the configuration and function of this compression encoder 1, quantization and coding techniques according to this preferred embodiment will be described in detail.

The compression encoder 1 comprises a DC level shift unit 10, a color-space conversion unit 11, a tiling unit 12, a DWT unit 13, a quantization unit 14, a ROI unit 15, a coefficient bit modeling unit 20, an arithmetic coding (entropy coding) unit 21, a rate control unit 22, an image-quality control unit 23 and a bit-stream generation unit 17.

All or parts of the units 10 to 15, 17 and 20 to 23 in the compression encoder 1 may consist of hardware or programs that run on a microprocessor.

An image signal inputted to the compression encoder 1 is DC level shifted in the DC level shift unit 10 as needed, and outputted to the color-space conversion unit 11. The color-space conversion unit 11 converts and outputs the color space of an input signal. The JPEG2000 standard provides reversible component transformation (RCT) and irreversible component transformation (ICT) for color-space conversion, either of which can be selected as necessary. Thus, for example, an input RGB signal is converted into a YcbCr or YUV signal.

Then, the tiling unit 12 divides the image signal inputted from the color-space conversion unit 11 into a plurality of rectangular regional components called “tiles” and outputs those components to the DWT unit 13. Here, the image signal is not always necessarily divided into tiles, and instead a single frame of image signal may be outputted as-is to the next functional block.

Then, the DWT unit 13 performs integer or real-number DWT on each tile of the image signal inputted from the tiling unit 12, thereby to recursively divide the image signal into high- and low-pass components according to the aforementioned octave band splitting method. As a result, transform coefficients in a plurality of bandpass components (subbands) HH1 to LL3 as shown in FIG. 26 are generated and outputted to the quantization unit 14. More specifically, the real-number DWT uses a 9/7-, 5/3-, or 7/5-tap filter, and the integer DWT uses a 5/3- or 13/7-tap filter. Such filtering may be implemented through a convolution operation or by a lifting scheme which is more efficient than the convolution operation.

The quantization unit 14 has the function of performing scalar quantization on transform coefficients inputted from the DWT unit 13 according to quantization parameters which are determined by the image-quality control unit 23. The quantization unit 14 also has the function of performing a bit-shift operation in which higher priority is assigned to the image quality of a region of interest (hereinafter referred to as a ROI) which is specified by the ROI unit 15. The method of performing quantization in the image-quality control unit 23 and the quantization unit 14, the method of ROI setting in the ROI unit 15 and the method of performing a bit-shift operation in the quantization unit 14 taking ROI into account will be described later in detail.

Then, transform coefficients QD outputted from the quantization unit 14 are entropy coded on a block-by-block basis in the coefficient bit modeling unit 20 and the arithmetic coding unit 21, and they are rate controlled in the rate control unit 22.

The coefficient bit modeling unit 20, like the coefficient bit modeling unit 108 shown in FIG. 25, divides each subband of input transform coefficients QD into code blocks of approximately size 32×32 or 64×64 and further decomposes each code block into a plurality of bit planes each constituting a 2-D array of bits. As a result, each code block is decomposed into a plurality of bit planes 122 ₀ through 122_(n−1) as shown in FIG. 28.

The arithmetic coding unit 21 performs arithmetic coding on coded data BD inputted from the coefficient bit modeling unit 20 and outputs resultant coded data AD to the rate control unit 22. The arithmetic coding unit 21 sometimes performs bypass processing in which part of data to be coded is not arithmetically coded but instead is outputted as-is as part of the coded data AD. While this preferred embodiment adopts the arithmetic coding, the present invention is not limited to this only and may adopt other techniques for entropy coding.

The rate control unit 22 has the function of controlling the rate of the coded data AD inputted from the arithmetic coding unit 21 according to instructions from the image-quality control unit 23. That is, the rate control unit 22 has the function of performing post-quantization in which the coded data AD is sequentially truncated in ascending order of priority on a subband-by-subband, bit-plane-by-bit-plane, or coding-pass-by-coding-pass basis.

The bit-stream generation unit 17 generates a bit stream by multiplexing coded data CD outputted from the rate control unit 22 and attached information (header information, layer structure, scalability, quantization table, etc.) and outputs it to the outside as a compressed image.

Quantization

Next, processing details of quantization achieved by the image-quality control unit 23 and quantization unit 14 shown in FIG. 1 are described.

The image-quality control unit 23 has the function of determining a quantization step size Δ_(b) for use in quantizing transform coefficients inputted from the DWT unit 13 in the quantization unit 14 on the basis of target quality information (high quality, standard quality, low quality, resolution information, etc.) which is provided from the outside. Hereinbelow, a method of determining the quantization step size Δ_(b) is described.

When an original image is divided by the DWT unit 13 into subbands (bandpass components) “XYn” (X and Y are either a high- or low-pass component H or L; n is the decomposition level) as shown in FIG. 26, the quantization step size Δ_(b) for use in quantization of each subband is given by the following equation (1): Δ_(b) =Q _(P) /Q _(b)  (1) where Q_(P) is a positive value inputted according to the target quality information, i.e., a quantization parameter; the higher the image quality, the smaller the input value. The quantization parameter Q_(P) may be specified by direct input of a numerical value from the user or, for example, a predetermined table may be provided which associates a predetermined keyword indicating target quality information such as high quality, standard quality, and low quality with each numerical value of the quantization parameter Q_(P), and then, the value of the quantization parameter Q_(P) may be read out from that table by the user specifying desired image quality of compressed image data by that keyword.

Further, Q_(b) is the quantized coefficient in each subband and expressed as a norm of a synthesis filter coefficient by the following equation (2): Q _(b)=√{square root over (G _(b))}  (2)

Here, the weighting factor G_(b) for subband b is calculated from the following equation (3): G _(b) =∥S _(b)∥², where S _(b) =s _(b)[n]  (3)

In the above equation (3), s_(b)[n] is the one-dimensional (1-D) synthesis filter coefficient for subband b, and ∥x∥ is the norm of the vector x.

According to the equations (4.39) and (4.40) given in the foregoing first no-patent literature, a 1-D synthesis filter coefficient S_(L[1])[n] for the low-pass component L1 of the first decomposition level and a 1-D synthesis filter coefficient S_(H[1])[n] for the high-pass component H1 of the same decomposition level are calculated from the following equations (4):

$\begin{matrix} \left\{ \begin{matrix} {{S_{L{\lbrack 1\rbrack}}\lbrack n\rbrack} = {g_{0}\lbrack n\rbrack}} \\ {{S_{H{\lbrack 1\rbrack}}\lbrack n\rbrack} = {g_{1}\lbrack n\rbrack}} \end{matrix} \right. & (4) \end{matrix}$

In the above equations (4), g₀[n] and g₁[n] are respectively low- and high-pass coefficients for a forward transform filter used in band splitting of image signals.

A 1-D synthesis filter coefficient S_(L[d])[n] for the low-pass component Ld of the d-th decomposition level (d=1, 2, . . . , D) and a 1-D synthesis filter coefficient S_(H[d])[n] for the high-pass component Hd of the same decomposition level are calculated from the following equations (5):

$\begin{matrix} \left\{ \begin{matrix} {{S_{L{\lbrack d\rbrack}}\lbrack n\rbrack} = {\sum\limits_{k}{{S_{L{\lbrack{d - 1}\rbrack}}\lbrack k\rbrack}{g_{0}\left\lbrack {n - {2k}} \right\rbrack}}}} \\ {{S_{H{\lbrack d\rbrack}}\lbrack n\rbrack} = {\sum\limits_{k}{{S_{H{\lbrack{d - 1}\rbrack}}\lbrack k\rbrack}{g_{0}\left\lbrack {n - {2k}} \right\rbrack}}}} \end{matrix} \right. & (5) \end{matrix}$

Then, the squared norm of the 1-D synthesis filter coefficient for the low-pass component Ld of the d-th decomposition level is calculated from the following equation (6):

$\begin{matrix} {G_{L{\lbrack d\rbrack}} = {{{S_{L{\lbrack d\rbrack}}\lbrack n\rbrack}}^{2} = {\sum\limits_{j}{{{S_{L{\lbrack d\rbrack}}\lbrack j\rbrack}^{2}}}}}} & (6) \end{matrix}$

Also, the squared norm of the 1-D synthesis filter coefficient for the high-pass component Hd can be calculated from a similar equation to the equation (6).

TABLE 1 gives the calculation results of the squared norms of 1-D synthesis filter coefficients. In the table, n is the decomposition level; for example, GL₁ shows the calculation result for the low-pass component L of the first decomposition level.

TABLE 1 Squared norms of 1D synthesis filter coefficients Decomposition Level GLn GHn 1 1.96591 0.52022 2 4.12241 0.96722 3 8.41674 2.07926 4 16.93557 4.30048 5 33.92493 8.68672 6 67.87717 17.41884 7 135.76805 34.86078 8 271.54296 69.73317 9 543.08936 139.47215 10 1086.18043 278.94721 11 2172.36172 557.89587

Two-dimensional (2-D) synthesis filter coefficients for subbands LLD, HLd, LHd, HHd of the d-th decomposition level (d=1, 2, . . . , D; D is an integer value) can be expressed by the product of the above 1-D synthesis filter coefficients, and a 2-D weighting factor G_(b) for subband b can be expressed by the product of the 1-D weighting factors. More specifically, the 2-D synthesis filter coefficients and the 2-D weighting factors can be calculated from the following equations (7):

$\begin{matrix} \left\{ \begin{matrix} {{S_{{LL}{\lbrack D\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{L{\lbrack D\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{L{\lbrack D\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{LL}{\lbrack D\rbrack}} \right. = {G_{L{\lbrack D\rbrack}} \cdot G_{L{\lbrack D\rbrack}}}}} \\ {{S_{{HL}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{L{\lbrack d\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{H{\lbrack d\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{HL}{\lbrack d\rbrack}} \right. = {G_{L{\lbrack d\rbrack}} \cdot G_{H{\lbrack d\rbrack}}}}} \\ {{S_{{LH}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{H{\lbrack d\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{L{\lbrack d\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{LH}{\lbrack d\rbrack}} \right. = {G_{H{\lbrack d\rbrack}} \cdot G_{L{\lbrack d\rbrack}}}}} \\ {{S_{{HH}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{H{\lbrack d\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{H{\lbrack d\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{HH}{\lbrack d\rbrack}} \right. = {G_{H{\lbrack d\rbrack}} \cdot G_{H{\lbrack d\rbrack}}}}} \end{matrix} \right. & (7) \end{matrix}$

In the above equations (7), the subscripts LL[D], HL[d], LH[d], and HH[d] stand for the subbands LLD, HLd, LHd, and HHd, respectively.

The square root of the weighting factor G_(b) is the norm. TABLEs 2 and 3 below give the calculation results of the 2-D weighting factors G_(b) obtained from TABLE 1. TABLE 2 gives the numerical values of the squared norms of each subband for the 9/7 filter (9/7-tap filter), and TABLE 3 gives the numerical values of the norms corresponding to TABLE 2.

TABLE 2 (Squared norms of) weighting coefficients G for distortion model for 9/7 filter Decomposition Level LL HL LH HH 1 3.86479 1.02270 1.02270 0.27063 2 16.99426 3.98726 3.98726 0.93551 3 70.84158 17.50056 17.50056 4.32330 4 286.81360 72.83113 72.83113 18.49415 5 1150.90066 294.69647 294.69647 75.45917 6 4607.30956 1182.34209 1182.34209 303.41630 7 18432.96262 4732.98083 4732.98083 1215.27440 8 73735.57967 18935.55202 18935.55202 4862.71528 9 294946.04918 75745.84127 75745.84127 19452.48118 10 1179787.92756 302986.99951 302986.99951 77811.54539 11 4719155.44117 1211951.63280 1211951.63280 311247.80240

TABLE 3 Norms of 9/7 filter Decomposition Level LL HL LH HH 1 1.96591 1.01129 1.01129 0.52022 2 4.12241 1.99681 1.99681 0.96722 3 8.41674 4.18337 4.18337 2.07926 4 16.93557 8.53412 8.53412 4.30048 5 33.92493 17.16673 17.16673 8.68672 6 67.87717 34.38520 34.38520 17.41885 7 135.76805 68.79666 68.79666 34.86079 8 271.54296 137.60651 137.60651 69.73317 9 543.08936 275.21962 275.21962 139.47215 10 1086.18043 550.44255 550.44255 278.94721 11 2172.36172 1100.88675 1100.88675 557.89587

For example, let the quantization parameter Q_(P)=16 for all of the luminance signal Y and the color difference signals U and V. Then, the quantization step sizes Δ_(b) for the luminance signal Y and the color difference signals U and V are obtained from the values given in TABLE 3 using the above equations (1) and (2), which are as shown in TABLE 4.

TABLE 4 Quantization step sizes Δ b Decomposition Level LL HL LH HH 1 X 15.82143 15.82143 30.75634 2 X 8.01277 8.01277 16.54233 3 X 3.82467 3.82467 7.69506 4 X 1.87483 1.87483 3.72051 5 0.47163 0.93204 0.93204 1.84189

The quantization parameter Q_(P) used in obtaining the quantization step size Δ_(b) for each of the luminance signal Y and the color difference signals U and V is not necessarily the same value, and different values may be used according to the contents of image data. For example, for enhancement of color components, the quantization parameter Q_(P) used for the color difference signals U and V may be smaller than that used for the luminance signal Y. In this way, an appropriate quantization parameter Q_(P) for each signal may be used in consideration of the contents of image data, and the like.

The image-quality control unit 23 obtains the quantization step size Δ_(b) in this way and gives it to the quantization unit 14. Then, the quantization unit 14 performs quantization with the given quantization step size Δ_(b) for each subband.

However, if the value of the quantization step size Δ_(b) is less than 1, it is multiplied by powers of 2 to obtain a value of 1 or more before quantization. For example, although the quantization step size Δ_(b) for the subband LL5 calculated by the aforementioned method is 0.47163, for actual quantization of image data, it is multiplied by 2² to obtain the value of 1.88652. Similarly, the quantization step size Δ_(b) of 0.93204 for the subband HL5 is multiplied by 2 to obtain the value of 1.86408 for quantization. In this way, the function of converting the quantization step size Δ_(b) into a predetermined numerical value depending on the performance of a quantizer for use in quantization simplifies the structure of a quantizer as well as achieves data compression that is the intended purpose of quantization. It should be noted here that making the quantization step size Δ_(b) a value of 1 or more is only one example. Thus, depending on the performance of a quantizer, for example if a quantizer uses the value of ½ or more, the quantization step size Δ_(b) should be converted into a value of ½ or more. That is, if the lower limit value handled by a quantizer is ½^(m), every quantization step size should be multiplied by powers of 2 to obtain a value of ½^(m) or more before quantization.

Instead of the aforementioned method, the image-quality control unit 23 can also determine the quantization step size Δ_(b) in consideration of human visual characteristics. This method is described hereinbelow.

The foregoing first non-patent literature describes in chapter 16 the weighted mean squared error (WMSE) based on the contrast sensitivity function (CSF) of the human visual system. Using this for improvement in human visual evaluation of image data after compression and coding, the above equation (2) is rewritten as the following equation (8): Q _(b)=√{square root over (W _(b[i]) ^(csf) G _(b[i]))}  (8) where W_(b[i]) ^(csf) if is called the “energy weighting factor” for subband b[i], the recommended numerical value of which is described in ISO/IEC JTC 1/SC 29/WG1 (ITU-T SG8) N2406, “JPEG 2000 Part 1 FDIS (including COR 1, COR2, and DCOR 3),” 4 Dec. 2001 (which is hereinafter referred to as the “second non-patent literature”). FIGS. 2 through 4 show the numerical values of the “energy weighting factors” described in the second non-patent literature.

In FIGS. 2 through 4, “level” and “Lev” stand for the decomposition level, and “Comp” stands for the luminance component Y and the color difference components Cb and Cr. Examples are shown for viewing distances of 1000, 1700, 2000, 3000, and 4000. The “Viewing distance of 1000”, “Viewing distance of 1700”, “Viewing distance of 2000”, “Viewing distance of 3000”, and “Viewing distance of 4000”, respectively, represent viewing distances when displays or prints of 100 dpi, 200 dpi, 300 dpi, and 400 dpi are viewed from 10 inches away.

For example, in the case of color image data, a specific method for obtaining the quantization step size Δ_(b) is described hereinbelow. Here, the color space of input color image consisting of RGB signals shall be converted by the color-space conversion unit 11 into YUV 422 or 420 color-space data.

In YUV 422 image data, the amount of data for the color difference signals U and V is one-half of that for the luminance signal Y, and in YUV 420 image data, it is one fourth. A wavelet plane of the luminance signal Y subjected to DWT is as shown in FIG. 5. Assuming that one half of the data amount is equivalent to one application of DWT in the horizontal direction to the wavelet plane shown in FIG. 5, the dotted area in FIG. 6 is the wavelet plane of the color difference signals U and V in YUV 422 format. Similarly, assuming that one fourth of the data amount is equivalent to each one application of DWT in the horizontal and vertical directions to the wavelet plane shown in FIG. 5, the dotted area in FIG. 7 is the wavelet plane of the color difference signals U and V in YUV 420 format.

In YUV 422 format, it is assumed that the horizontal component is subjected to one more filtering than the vertical component as shown in FIG. 6. Thus, the above equations (7) for calculating the 2-D synthesis filter coefficients and the 2-D weighting coefficients can be rewritten as the following equations (9):

$\begin{matrix} \left\{ \begin{matrix} {{S_{{LL}{\lbrack D\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{L{\lbrack D\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{L{\lbrack{D + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{LL}{\lbrack D\rbrack}} \right. = {G_{L{\lbrack D\rbrack}} \cdot G_{L{\lbrack{D + 1}\rbrack}}}}} \\ {{S_{{HL}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{L{\lbrack d\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{H{\lbrack{d + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{HL}{\lbrack d\rbrack}} \right. = {G_{L{\lbrack d\rbrack}} \cdot G_{H{\lbrack{d + 1}\rbrack}}}}} \\ {{S_{{LH}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{H{\lbrack d\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{L{\lbrack{d + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{LH}{\lbrack d\rbrack}} \right. = {G_{H{\lbrack d\rbrack}} \cdot G_{L{\lbrack{d + 1}\rbrack}}}}} \\ {{S_{{HH}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{H{\lbrack d\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{H{\lbrack{d + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{HH}{\lbrack d\rbrack}} \right. = {G_{H{\lbrack d\rbrack}} \cdot G_{H{\lbrack{d + 1}\rbrack}}}}} \end{matrix} \right. & (9) \end{matrix}$

Similarly, in YUV 420 format, it is assumed that both the horizontal and vertical components are subjected to one more filtering as shown in FIG. 7. Thus, the above equations (7) can be rewritten as the following equations (10):

$\begin{matrix} \left\{ \begin{matrix} {{S_{{LL}{\lbrack D\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{L{\lbrack{D + 1}\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{L{\lbrack{D + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{LL}{\lbrack D\rbrack}} \right. = {G_{L{\lbrack{D + 1}\rbrack}} \cdot G_{L{\lbrack{D + 1}\rbrack}}}}} \\ {{S_{{HL}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{L{\lbrack{d + 1}\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{H{\lbrack{d + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{HL}{\lbrack d\rbrack}} \right. = {G_{L{\lbrack{d + 1}\rbrack}} \cdot G_{H{\lbrack{d + 1}\rbrack}}}}} \\ {{S_{{LH}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{H{\lbrack{d + 1}\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{L{\lbrack{d + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{LH}{\lbrack d\rbrack}} \right. = {G_{H{\lbrack{d + 1}\rbrack}} \cdot G_{L{\lbrack{d + 1}\rbrack}}}}} \\ {{S_{{HH}{\lbrack d\rbrack}}\left\lbrack {n_{1},n_{2}} \right\rbrack} = {\left. {{S_{H{\lbrack{d + 1}\rbrack}}\left\lbrack n_{1} \right\rbrack}{S_{H{\lbrack{d + 1}\rbrack}}\left\lbrack n_{2} \right\rbrack}}\Rightarrow G_{{HH}{\lbrack d\rbrack}} \right. = {G_{H{\lbrack{d + 1}\rbrack}} \cdot G_{H{\lbrack{d + 1}\rbrack}}}}} \end{matrix} \right. & (10) \end{matrix}$

Using the above equations (9) and (10) and the values given in TABLE 1, the norms of the color difference signals in YUV 422 and 420 formats are obtained, the results of which are shown in TABLEs 5 and 6, respectively.

TABLE 5 Norms of color difference signals in YUV 422 format Decomposition Level LL HL LH HH 1 2.84680 1.378933 1.46443 0.70934 2 5.89044 2.92722 2.85321 1.41813 3 11.93911 6.01632 5.93409 2.99028 4 23.96952 12.12908 12.07864 6.11205 5 47.98675 24.30912 24.28230 12.30092 6 95.99766 48.64413 48.63048 24.64213 7 192.00744 97.30127 97.29440 49.30780 8 384.02095 194.60905 194.60561 98.61965 9 768.04494 389.22135 389.21963 197.24444 10 1536.09140 778.44433 778.44347 394.49144

TABLE 6 Norms of color difference signals in YUV 420 format Decomposition Level LL HL LH HH 1 4.12241 1.996813 1.99681 0.96722 2 8.41674 4.18337 4.18337 2.07926 3 16.93557 8.53412 8.53412 4.30048 4 33.92493 17.16673 17.16673 8.68672 5 67.87717 34.38520 34.38520 17.41885 6 135.76805 68.79666 68.79666 34.86079 7 271.54296 137.60651 137.60651 69.73317 8 543.08936 275.21962 273.21962 139.47215 9 1086.18043 550.44255 550.44255 278.94721 10 2172.36172 1100.88675 1100.88675 557.89587

Next, according to the description of the first non-patent literature, the energy weighting factor W_(b[i]) ^(csf) for subband b[i] can be expressed as the product of energy weighting factors for that subband in the horizontal and vertical directions, which can be expressed by the following equations (11):

$\begin{matrix} \left\{ \begin{matrix} {\sqrt{W_{{LL}{\lbrack D\rbrack}}^{csf}} = 1} \\ {\sqrt{W_{{HL}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{L{\lbrack d\rbrack}}^{csf}} \cdot \sqrt{W_{H{\lbrack d\rbrack}}^{csf}}}} \\ {\sqrt{W_{{LH}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{H{\lbrack d\rbrack}}^{csf}} \cdot \sqrt{W_{L{\lbrack d\rbrack}}^{csf}}}} \\ {\sqrt{W_{{HH}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{H{\lbrack d\rbrack}}^{csf}} \cdot \sqrt{W_{H{\lbrack d\rbrack}}^{csf}}}} \end{matrix} \right. & (11) \end{matrix}$

The energy weighting factor for the luminance signal Y in YUV 422 or 420 image data can be obtained from the above equations (11). In the YUV 444 format, all the energy weighting factors for the luminance signal and the color difference signals can be obtained from the above equations (11).

For the color difference signals U and V in YUV 422 format, since it is assumed as above described that the horizontal component is subjected to one more filtering than the vertical component, energy weighting factors for those signals can be expressed by the following equations (12), instead of the above equations (11).

$\begin{matrix} \left\{ \begin{matrix} {\sqrt{W_{{LL}{\lbrack D\rbrack}}^{csf}} = 1} \\ {\sqrt{W_{{HL}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{L{\lbrack d\rbrack}}^{csf}} \cdot \sqrt{W_{H{\lbrack{d + 1}\rbrack}}^{csf}}}} \\ {\sqrt{W_{{LH}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{H{\lbrack d\rbrack}}^{csf}} \cdot \sqrt{W_{L{\lbrack{d + 1}\rbrack}}^{csf}}}} \\ {\sqrt{W_{{HH}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{H{\lbrack d\rbrack}}^{csf}} \cdot \sqrt{W_{H{\lbrack{d + 1}\rbrack}}^{csf}}}} \end{matrix} \right. & (12) \end{matrix}$

Similarly, for the color difference signals U and V in YUV 420 format, since it is assumed that both the horizontal and vertical components are subjected to one more filtering, energy weighting factors for those signals can be expressed by the following equations (13), instead of the above equations (11).

$\begin{matrix} \left\{ \begin{matrix} {\sqrt{W_{{LL}{\lbrack D\rbrack}}^{csf}} = 1} \\ {\sqrt{W_{{HL}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{L{\lbrack{d + 1}\rbrack}}^{csf}} \cdot \sqrt{W_{H{\lbrack{d + 1}\rbrack}}^{csf}}}} \\ {\sqrt{W_{{LH}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{H{\lbrack{d + 1}\rbrack}}^{csf}} \cdot \sqrt{W_{L{\lbrack{d + 1}\rbrack}}^{csf}}}} \\ {\sqrt{W_{{HH}{\lbrack d\rbrack}}^{csf}} = {\sqrt{W_{H{\lbrack{d + 1}\rbrack}}^{csf}} \cdot \sqrt{W_{H{\lbrack{d + 1}\rbrack}}^{csf}}}} \end{matrix} \right. & (13) \end{matrix}$

The values of the energy weighting factors for the color difference signals U and V for “Viewing distance of 1000”, “Viewing distance of 1700”, “Viewing distance of 3000”, obtained from the description of the second non-patent literature, are shown in TABLEs 7 to 9. In those and following tables, Cb and Cr represent the color difference signals U and V, respectively.

TABLE 7 Energy weighting factors {square root over (W_(b[i]) ^(csf))} (Viewing distance 1000) Decomposition Level {square root over (W_(L[n]) ^(csf))} {square root over (W_(H[n]) ^(csf))} Cb 1 0.68333 0.33732 2 0.81063 0.55604 3 0.89207 0.72918 4 0.94018 0.84398 5 0.96735 0.91301 Cr 1 0.75074 0.44778 2 0.85423 0.64725 3 0.91782 0.79063 4 0.95462 0.88101 5 0.97523 0.93401

TABLE 8 Energy weighting factors {square root over (W_(b[i]) ^(csf))} (Viewing distance 1700) Decomposition Level {square root over (W_(L[n]) ^(csf))} {square root over (W_(H[n]) ^(csf))} Cb 1 0.55396 0.17658 2 0.71767 0.39024 3 0.83345 0.60190 4 0.90584 0.76107 5 0.94801 0.86364 Cr 1 0.63889 0.27772 2 0.77922 0.49856 3 0.87223 0.68622 4 0.92840 0.81606 5 0.96060 0.89620

TABLE 9 Energy weighting factors {square root over (W_(b[i]) ^(csf))} (Viewing distance 3000) Decomposition Level {square root over (W_(L[n]) ^(csf))} {square root over (W_(H[n]) ^(csf))} Cb 1 0.39897 0.05842 2 0.58653 0.21145 3 0.74224 0.43082 4 0.84937 0.63510 5 0.91531 0.78344 Cr 1 0.49254 0.12238 2 0.66780 0.31727 3 0.79932 0.53628 4 0.84470 0.71395 5 0.93565 0.83374

Using the values given in TABLEs 7 to 9 and the above equations (11) to (13), energy weighting factors for image data in YUV 422 and 420 formats are obtained, which are shown in TABLEs 10 to 12 and 13 to 15, respectively.

TABLE 10 Energy weighting factors {square root over (W_(b[i]) ^(csf))} in YUV 422 format (Viewing distance 1000) Decomposition Level LL HL LH HH Y 1 X 0.75635 0.75635 0.57306 2 X 0.99828 0.99828 0.99656 3 X 1 1 1 4 X 1 1 1 5 1 1 1 1 Cb 1 X 0.37996 0.27344 0.18756 2 X 0.59109 0.49603 0.40545 3 X 0.75289 0.68556 0.61541 4 1 0.85839 0.81642 0.77056 5 Cr 1 X 0.48592 0.38251 0.28983 2 X 0.67538 0.59406 0.51174 3 X 0.80861 0.75476 0.69656 4 1 0.89163 0.85919 0.82287 5

TABLE 11 Energy weighting factors {square root over (W_(b[i]) ^(csf))} in YUV 422 format (Viewing distance 1700) Decomposition Level LL HL LH HH Y 1 X 0.30719 0.30719 0.10892 2 X 0.86159 0.86159 0.75234 3 X 1 1 1 4 X 1 1 1 5 1 1 1 1 Cb 1 X 0.21617 0.12672 0.06891 2 X 0.43197 0.32525 0.23489 3 X 0.63431 0.54522 0.45808 4 1 0.78232 0.72152 0.65729 5 Cr 1 X 0.31853 0.216414 0.13846 2 X 0.53471 0.43486 0.34212 3 X 0.71179 0.63708 0.55999 4 1 0.83203 0.78390 0.73135 5

TABLE 12 Energy weighting factors {square root over (W_(b[i]) ^(csf))} in YUV 422 format (Viewing distance 3000) Decomposition Level LL HL LH HH Y 1 X 0.03818 0.03849 0.00308 2 X 0.41063 0.41063 0.18276 3 X 0.92105 0.92105 0.84832 4 X 1 1 1 5 1 1 1 1 Cb 1 X 0.08436 0.03427 0.01235 2 X 0.25269 0.15695 0.09110 3 X 0.47139 0.36593 0.27362 4 1 0.66543 0.58131 0.49756 5 Cr 1 X 0.15627 0.08173 0.03883 2 X 0.35813 0.25360 0.17014 3 X 0.57068 0.47444 0.38288 4 1 0.73761 0.66801 0.59525 5

TABLE 13 Energy weighting factors {square root over (W_(b[i]) ^(csf))} in YUV 420 format (Viewing distance 1000) Decomposition Level LL HL LH HH Y 1 X 0.75635 0.75635 0.57306 2 X 0.99828 0.99828 0.99656 3 X 1 1 1 4 X 1 1 1 5 1 1 1 1 Cb 1 X 0.45074 0.45074 0.30918 2 X 0.65048 0.65048 0.5317 3 X 0.79349 0.79349 0.71230 4 1 0.88320 0.88320 0.83358 5 Cr 1 X 0.55290 0.55290 0.41894 2 X 0.72566 0.72566 0.62510 3 X 0.84103 0.84103 0.77618 4 1 0.91088 0.91088 0.87238 5

TABLE 14 Energy weighting factors {square root over (W_(b[i]) ^(csf))} in YUV 420 format (Viewing distance 1700) Decomposition Level LL HL LH HH Y 1 X 0.30719 0.30719 0.10892 2 X 0.86159 0.86159 0.74234 3 X 1 1 1 4 X 1 1 1 5 1 1 1 1 Cb 1 X 0.28007 0.28006 0.15229 2 X 0.50165 0.50165 0.36228 3 X 0.68940 0.68940 0.57922 4 1 0.81877 0.81876 0.74588 5 Cr 1 X 0.38849 0.38849 0.24857 2 X 0.59854 0.59854 0.47089 3 X 0.75763 0.75763 0.66595 4 1 0.86089 0.86089 0.80317 5

TABLE 15 Energy weighting factors {square root over (W_(b[i]) ^(csf))} in YUV 420 format (Viewing distance 3000) Decomposition Level LL HL LH HH Y 1 X 0.03849 0.03849 0.00308 2 X 0.41063 0.41063 0.18276 3 X 0.92105 0.92105 0.84832 4 X 1 1 1 5 1 1 1 1 Cb 1 X 0.12402 0.12402 0.04471 2 X 0.31977 0.31977 0.18561 3 X 0.53943 0.53944 0.40335 4 1 0.71709 0.71709 0.61378 5 Cr 1 X 0.21187 0.21187 0.10066 2 X 0.42866 0.42866 0.28759 3 X 0.63163 0.63163 0.50973 4 1 0.78009 0.78009 0.69513 5

Substituting the values of the norms given in TABLEs 5 and 6 into the above equations (1) and (2) yields a normalized quantization step size Δ_(b); and substituting the values of the norms given in TABLEs 5 and 6 and the values of the energy weighting factors given in TABLEs 10 through 15 into the above equations (1) and (8) yields a visually weighted quantization step size Δ_(b) which takes into account the human visual characteristics.

For example, let the quantization parameter Q_(P)=16 for all of the luminance signal Y and the color difference signals U and V. Then, the quantization step sizes Δ_(b) for the luminance signal Y and the color difference signals U and V when visual weighting optimized for a viewing distance of 3000 is applied to YUV 422 color image data are obtained by using the values of the norms given in TABLE 5, the values of the energy weighting factors given in TABLE 12, and the above equations (1) and (8). The results are shown in TABLEs 16 to 18.

TABLE 16 Quantization step sizes Δ b for luminance signal Y in YUV 422 format (Viewing distance 3000) Decomposition Level LL HL LH HH 1 X 411.08509 411.08509 10002.06109 2 X 19.51345 19.51345 90.51394 3 X 4.15253 4.15253 9.07090 4 X 1.87483 1.87483 3.72051 5 0.47163 0.93204 0.93204 1.84189

TABLE 17 Quantization step sizes Δ b for color difference signal U(Cb) in YUV 422 format (Viewing distance 3000) Decomposition Level LL HL LH HH 1 X 137.54117 4318.85669 1825.95586 2 X 21.62733 35.73031 123.85047 3 X 5.64163 7.36832 19.55534 4 0.66751 1.98239 2.27873 5.26121

TABLE 18 Quantization step sizes Δ b for color difference signal V(Cr) in YUV 422 format (Viewing distance 3000) Decomposition Level LL HL LH HH 1 X 74.25290 133.68785 580.93701 2 X 15.25998 22.11257 66.31184 3 X 4.66012 5.68306 13.97490 4 0.66751 1.78840 1.98298 4.39776

Here, the quantization parameter Q_(P) used in obtaining the quantization step size Δ_(b) for each of the luminance signal Y and the color difference signals U and V is not necessarily the same value, and different values may be used according to the contents of image data. For example, for enhancement of color components, the quantization parameter Q_(P) used for the color difference signals U and V may be smaller than that used for the luminance signal Y. In this way, an appropriate quantization parameter Q_(P) for each signal may be used in consideration of the contents of image data and the like.

The image-quality control unit 23 obtains the quantization step size Δ_(b) in this way and gives it to the quantization unit 14. Then, the quantization unit 14 performs quantization with the given quantization step size Δ_(b) for each subband. At this time, if the quantization step size Δ_(b) is less than 1, as previously described, it is multiplied by powers of 2 to obtain a value of 1 or more before quantization.

As so far described, the image-quality control method according to this preferred embodiment implements image quality control by quantization and thereby allows precise control according to target image quality. Since there is no need for complicated processes such as finding an optimal solution, high speed processing is allowed with minimal operations. Besides, it is also possible to generate a compressed image with high display image quality in consideration of the human visual characteristics.

ROI Setting

The ROI unit 15 shown in FIG. 1 specifies, in image data, a region where degradation in image quality resulting from rate control to be described later is desired to be avoided or suppressed, as a ROI.

Hereinbelow, a method of specifying a ROI is described.

The ROI unit 15 gives a mask signal for specifying an important region as a ROI, to image data having been subjected to wavelet transform. For example, to specify only a face region below the forehead as a ROI in image data (original image) 30 of a person as shown in FIG. 8, a single mask region 31 is defined as shown by a hollow portion in FIG. 9A, and this region is specified as a ROI.

This mask region 31 can be specified using a pointing input device such as a so-called mouse in correspondence with the original image 30 while looking at the original image 30 on the screen of a display. An alternative form may be adopted in which the mask region 31 is automatically specified by analysis of image data, for example, by extracting a predetermined region including a skin-colored region.

While FIG. 9A shows an example of specifying a single ROI on the original image 30, a plurality of regions such as a left eye region, a right eye region, and a region including nose and mouth may be specified as ROIs as shown by hollow portions in FIG. 9B. In that case, these regions are specified by different mask signals, respectively.

Then, the remaining region with the mask region 31 being excluded is specified as a non-ROI 32 (see FIGS. 9A and 9B).

In the case where a plurality of mask signals are given, priorities may be assigned to the plurality of mask signals. The higher the priority, the greater the volume of information, for example, the higher the bit rate, which reduces losses in expansion.

Then, the mask region 31 is expanded into a wavelet plane to generate a mask signal.

Here, the method of converting a mask signal into a region corresponding to a wavelet plane depends upon the number of taps of filter for use in wavelet transform.

For example, let a reversible 5/3-tap filter (having a low-pass filter with 5 taps on the decomposition side and a high-pass filter with 3 taps on the decomposition side) is applied to arithmetic operations in wavelet transform, as shown in FIG. 11. Then, in the case where even-numbered or 2n-th pixel data of an original image is specified as a ROI, a mask signal is expanded into a wavelet plane assuming that the n-th data in a low-pass filter 33 and the (n−1)- and n-th data in a high-pass filter 34 represent a ROI. In the case where odd-numbered or (2n+1)-th pixel data of an original image is specified as a ROI, a mask signal is expanded into a wavelet plane assuming that the n- and (n+1)-th data in the low-pass filter 33 and the (n−1)-, n-, and (n+1)-th data in the high-pass filter 34 represent a ROI. Although FIG. 11 only shows the correspondence between an original image and the first-level wavelet plane, similar recursive expansion is also performed in expansion into deeper levels.

Alternatively, for example, let a Daubechies 9/7-tap filter (having a low-pass filter with 9 taps on the decomposition side and a high-pass filter with 7 taps on the decomposition side) is applied to arithmetic operations in wavelet transform, as shown in FIG. 12. Then, in the case where even-numbered or 2n-th pixel data of an original image is specified as a ROI, a mask signal is expanded into a wavelet plane assuming that the (n−1)-, n-, and (n+1)-th data of the low-pass filter 33 and the (n−2)-, (n−1)-, n-, and (n+1)-th data in the high-pass filter 34 represent a ROI. In the case where odd-numbered or (2n+1)-th pixel data in an original image is specified as a ROI, a mask signal is expanded into a wavelet plane assuming that the (n−1)-, n-, (n+1)-, and (n+2)-th data in the low-pass filter 33 and the (n−2)-, (n−1)-, n-, (n+1)-, and (n+2)-th data in the high-pass filter 34 represent a ROI. Although FIG. 12 only shows the correspondence between an original image and the first-level wavelet plane, similar recursive expansion is also performed in expansion into deeper levels.

Referring to the correspondences shown in FIGS. 11 and 12, a mask signal is expanded into a wavelet plane considering a region, if any, where a non-ROI specified relative to a particular piece of pixel data of an original image and a ROI specified relative to another piece of pixel data of the original image overlap each other, as a ROI. While the method of converting a mask signal has been discussed in each case of the reversible 5/3-tap filter and Daubechies 9/7-tap filter, rate control will be described later using the Daubechies 9/7-tap filter by way of example.

A hollow region 31 a shown in FIG. 10A is a region (hereinafter referred to as an “expanded mask region”) obtained by expanding the mask region (ROI) 31 shown in FIG. 9A into a wavelet plane in the above-described manner. A mask signal corresponding to this expanded mask region 31 a is generated and given to image data having been subjected to wavelet transform. The character 32 a shown in FIG. 10A denotes a region (hereinafter referred to as an “expanded non-mask region”) obtained by expanding the non-ROI 32 into the wavelet plane. In an area where a mask region (ROI) 31 and a non-ROI 32 overlap each other, the mask region (ROI) 31 is selected and allocated to such area. That is, a region assigned higher priority is selected and allocated to such area.

In the case where a plurality of ROIs are specified as mask regions 31 as shown in FIG. 9B, a plurality of mask regions 31 a are expanded into a wavelet plane as shown in FIG. 10B. In such case of the plurality of mask regions 31 a expanded into a wavelet plane, priority setting may be performed at this stage. As previously described, when a plurality of ROIs 31 are specified in an original image, each expanded mask region 31 a is generated on the wavelet plane for each ROI 31. All expanded mask regions 31 a that correspond to one ROI 31 are assigned the same degree of priority on the basis of the priority assigned to the ROI 31, and finally, all of the expanded mask regions 31 a are assigned priorities.

When a plurality of ROIs 31 are specified in an original image, a plurality of expanded mask regions 31 a may overlap one another in a region passed through the low-pass filter 33 on the wavelet plane. In such case, priority is determined considering the overlapping region as an expanded mask region 31 a assigned the highest priority among the plurality of expanded mask regions 31 a overlapping one another.

A region for which no mask signal is generated in image data having been subjected to wavelet transform shall be an expanded non-mask region 32 a. The expanded non-mask region 32 a is assigned lower priority than all the expanded mask regions 31 a.

In this way, a plurality of ROIs may be specified, and each of the ROIs may be assigned priority.

Information on specified ROIs (ROI information) is inputted to the quantization unit 14.

Sorting and Bit Shifting

The quantization unit 14 shown in FIG. 1 performs sorting and bit shifting on quantized data based on the quantization step size Δ_(b) and ROI information given by the aforementioned ROI unit 15.

Allocating higher rate preferentially to data quantized with smaller quantization step sizes Δ_(b) and to ROI data and bit shifting these data so as not to be affected by truncation in the rate control, degradation in image quality of ROIs caused by coding processing can be avoided or suppressed.

Hereinbelow, sorting and bit shifting based on the quantization step size is described.

First, when a predetermined value of the quantization parameter Q_(P) is specified as target image quality, the image-quality control unit 23 calculates the quantization step size Δ_(b) based on this value by the aforementioned method and gives it to the quantization unit 14.

Upon receipt of the quantization step size Δ_(b), the quantization unit 14, based on this value, quantizes image data having been subjected to DWT in the DWT unit 13.

Then, the quantization unit 14 sorts quantized data in ascending order of quantization step size Δ_(b).

When the data is quantized with the quantization step size Δ_(b) which has been converted into a value of 1 or more as previously described, the sorting is performed according to the quantization step size Δ_(b) before quantization; however, at this time, the data is shifted to the left by the number of bits corresponding to the exponent of the powers of 2 used for multiplication to convert the quantization step size Δ_(b). A specific form of processing is described hereinbelow.

For example, the quantization step size Δ_(b) for the subband LL5 in TABLE 4 is 0.47163, but for actual quantization of image data, this value is multiplied by 2² to obtain the value of 1.88652. Therefore, data in the subband LL5 is shifted to the left by 2 bits in correspondence with the exponent of 2² used for multiplication to convert the quantization step size Δ_(b). Similarly, the quantization step size Δ_(b) of 0.93204 for the subband HL5 is multiplied by 2 to obtain the value of 1.86408 for quantization. Therefore, the coded data AD in the subband HL5 is shifted to the left by 1 bit in correspondence with the exponent of 2 used for multiplication. That is, when quantization is performed with the quantization step size Δ_(b) multiplied by 2^(m), data concerned is shifted to the left by the number of bits corresponding to the exponent m, whereby the priority of data is controlled.

FIG. 13 shows transform coefficients subjected to such a bit-shift operation based on the quantization step size Δ_(b) shown in TABLE 4. In the figure, each part of the code sequence marked with an asterisk indicates that the value of the quantization step size Δ_(b) is converted for quantization, and the numbers 0 through 9 on each bit of the code sequence indicate a number of a bit plane to which that bit belongs. Here, the LSB number is 0, and the MSB number is 9.

Then, the transform coefficients are sorted in ascending order of the quantization step size Δ_(b) used for quantization. In FIG. 13, the values of the quantization step sizes Δ_(b) in the parts indicated by the arrows are not in ascending order; thus, those parts are sorted. The code sequence sorted in this way is shown in FIG. 14. The arrows in FIG. 14 indicate parts of the code sequence whose positions are shifted from their positions in FIG. 13.

Similar processing can also be performed in the case of color images and in the case where the quantization step size Δ_(b) is calculated by applying visual weighting.

For example, if, as previously described, the quantization parameter Q_(P)=16 and visual weighting optimized for a viewing distance of 3000 is applied to YUV 422 color image data, the quantization step sizes Δ_(b) for the luminance signal Y and the color difference signals U and V are as shown in TABLEs 16 to 18.

At this time, the quantization step sizes Δ_(b) of less than 1 in TABLEs 16 to 18 are, as previously described, multiplied by powers of 2 for quantization. Then, the data which has been quantized with the converted quantization step size Δ_(b) is shifted to the left by the number of bits corresponding to the exponent of the powers of 2 used for multiplication of the original quantization step size Δ_(b).

In the case of color images, there are data on each of the luminance signal Y and the color difference signals U and V; however, all those data are sorted together in ascending order of the quantization step size Δ_(b) without being classified by signal. Resultant transform coefficients are shown in FIG. 15. In the figure, YLL5 represents data on the luminance signal Y in the subband LL5.

In this way, all the data on the luminance signal Y and the color difference signals U and V are subjected to aforementioned bit shifting and sorting processes.

Now, bit shifting based on the ROI information is described hereinbelow.

As previously described, the present invention achieves processing in the cases of monochrome images and color images, and further, in the cases where visual weighting are applied to those images. The specifics of processing on YUV 422 color image data are described hereinbelow in the case where the quantization parameter Q_(P)=16 as shown in FIG. 15 and visual weighting optimized for a viewing distance of 3000 is applied to the color images.

First, as previously described, the ROI unit 15 specifies a ROI and a non-ROI and gives information on setting these regions to the quantization unit 14. The setting information contains information as to whether each pixel data represents a ROI or non-ROI as well as information related to priorities, if assigned to a plurality of ROIs. The present embodiment, however, only uses the information as to whether each pixel data represents a ROI or not.

The quantization unit 14 divides the transform coefficients shown in FIG. 15 further into ROI data and non-ROI data based on received ROI information.

More specifically, FIG. 15 shows the transform coefficients having been subjected to bit shifting and sorting with the quantization step size Δ_(b) as previously described, for each of the subbands shown in FIG. 5. As is apparent from comparison between FIG. 5 and FIGS. 10A and 10B, the transform coefficient for each of the subbands shown in FIG. 15 actually includes data on the ROIs 31 a and data on the non-ROI 32 a shown in FIG. 10A or 10B.

The quantization unit 14 divides the transform coefficient for each of the subbands shown in FIG. 15 into ROI data 35 a and non-ROI data 35 b as shown in FIG. 16, based on the information on specifying the ROIs 31 a given by the ROI unit 15.

Then, the quantization unit 14 bit shifts the transform coefficients for the ROI data 35 a to the left. More specifically, as shown in FIG. 17, the quantization unit 14 shifts the transform coefficients for the ROI data 35 a to the left by the number of bits greater than the maximum bit length of the transform coefficients for the non-ROI data 35 b, for example, by 12 bits.

Bit shifting of the transform coefficients for the ROI data 35 a by the number of bits greater than the maximum bit length of the transform coefficients for the non-ROI data 35 b can be implemented by the Max-shift method provided as an optional function of JPEG2000.

The transform coefficients QD outputted from the quantization unit 14 are entropy coded on a block-by-block basis in the coefficient bit modeling unit 20 and arithmetic coding unit 21, and they are rate controlled in rate control unit 22.

Rate Control

The rate control unit 22 controls the rate of the coded data AD having been quantized in the quantization unit 14 and processed in the coefficient bit modeling unit 20 and arithmetic coding unit 21.

Using the sorted and bit-shifted code sequence as shown in FIG. 17, the rate control unit 22 truncates data so that a total capacity of data falls within a predetermined capacity. The data truncation occurs in sequence from the rightmost bit. For example, in the case of FIG. 17, data is truncated from data of bit 0 in the subband VHL4 in the code sequence for the non-ROI data 35 b downwardly in sequence through data of bit 0 in the subband YHH5, and so on. If the total capacity of data can be managed within a predetermined capacity which meets a target rate by truncation of up to bit data in the subband UHH1, data in the dotted area in FIG. 17 will be truncated. If the total capacity of data cannot be managed within the predetermined capacity by truncation of up to bit data in the subband YHH1, data will be truncated from data of bit 0 in the subband VLL4 in the code sequence for the non-ROI data 35 b downwardly in sequence through data of bit 0 in the subband ULL4, data of bit 0 in the subband YLH5, and so on. The data truncation is continued in sequence from data of the rightmost bit to data of bit 9 in the subband YLL5 in the code sequence for the ROI data 35 a until the total capacity of data meets the target rate.

The bit data (bit plane) can be divided into the SIG pass, MR pass and CL pass, as shown in FIG. 29. The rate control performed by the rate control unit 22 may be implemented on a bit-plane-by-bit-plane basis as previously described, or alternatively, on a pass-by-pass basis.

In such case, the rate control unit 22 will truncate CL passes from the CL pass of number 0 in the subband VHL4 in the code sequence for the non-ROI data 35 b downwardly in sequence through the CL pass of number 0 in the subband YHH5, and so on. If the total capacity of data cannot be managed within a predetermined capacity which meets a target rate by truncation of up to the CL pass of number 0 in the subband YHH1, the process will subsequently go back to the subband VHL4 in the code sequence for the non-ROI data 35 b, and MR passes will be truncated from the MR pass of number 0 in the subband VHL4 downwardly in sequence through the MR pass of number 0 in the subband YHH5, the MR pass of number 0 in the subband YHH1, and so on. The data truncation is continued in sequence from the CL pass of data of the rightmost bit to the CL pass of data of number 9 in the subband YLL5 in the code sequence for the ROI data 35 a on a pass-by-pass basis until the total capacity of data meets the target rate.

Now, it should be noted that if, at a stage of being inputted to the rate control unit 22, the total capacity of the coded data AD is already equal to or smaller than a target rate intended by the user, the aforementioned rate control is not necessary.

In this way, bit data or pass in each subband, sorted and bit shifted with the value of the quantization step size Δ_(b), and further sorted and bit shifted based on the ROI information, is truncated from the lower-order bits, by which rate control is achieved.

As a result, the amount of data can be precisely controlled.

Unlike the conventional method, the rate control process according to this preferred embodiment eliminates the necessity of calculating the amount of distortion in each coding pass for rate-distortion optimization and thereby achieves highly efficient rate control with high immediacy and with significantly reduced overhead.

It should also be noted that the data truncation occurs in sequence from the code sequence for the non-ROI data 35 b, and only if the total data capacity cannot be managed within a predetermined data capacity by truncation of all the code sequence for the non-ROI data 35 b, the code sequence for the ROI data 35 a is subjected to truncation. This can avoid or suppress ROI information from being lost due to the rate control, achieving compression and coding with the image quality of a ROI being maintained.

Further, sorting and bit shifting based on the ROI information can be implemented by the Max-shift method which is specified as an optional function of JPEG2000, and is thus readily applicable to the conventional compression encoder.

Second Preferred Embodiment

According to the first preferred embodiment, at the stage of quantization, data is sorted and bit shifted based on the quantization step size, and sorted and bit shifted based on ROI information.

In contrast, according to this second preferred embodiment, the quantization unit 14 only performs quantization based on the ROI information. Then, after being outputted from the quantization unit 14 and entropy coded on a block-by-block basis in the coefficient bit modeling unit 20 and arithmetic coding unit 21, data is sorted and bit shifted with the quantization step size, and sorted and bit shifted based on the ROI information, in the rate control performed by the rate control unit 22.

Hereinbelow, the configuration and operation of a compression encoder according to the present embodiment is described, particularly directing to differences from the first preferred embodiment.

Compression Encoder

FIG. 18 is a functional block diagram showing a general configuration of a compression encoder 50 according to the present embodiment. The compression encoder 50 has a similar configuration to the compression encoder according to the first preferred embodiment.

The present embodiment differs from the first preferred embodiment in that ROI information specified by the ROI unit 15 is inputted to the rate control unit 22, rather than to the quantization unit 14.

The quantization unit 14 only performs quantization based on the quantization step size Δ_(b) among the processes previously described in the first preferred embodiment.

The ROI unit 15 specifies a ROI as described in the first preferred embodiment; however, ROI setting is performed for each of code blocks obtained by dividing image data having been subjected to DWT and expanded into a wavelet plane, into rectangular regions of predetermined size.

The transform coefficients QD outputted from the quantization unit 14 are entropy coded on a block-by-block basis in the coefficient bit modeling unit 20 and arithmetic coding unit 21, and they are rate controlled in the rate control unit 22.

At this time, according to the first preferred embodiment, the quantization unit 14 performs sorting and bit shifting with the quantization step size Δ_(b) and sorting and bit shifting based on the ROI information given by the ROI unit 15; however, according to this second preferred embodiment, the rate control unit 22 performs these operations as preliminary processing prior to the rate control.

The respective components of the compression encoder 50 function and operate similarly to the compression encoder according to the first preferred embodiment, and redundant explanation is thus omitted here.

Quantization

The image-quality control unit 23 determines a quantization step size on the basis of target image-quality information which is supplied from the outside, and the quantization unit 14 performs quantization on transform coefficients inputted from the DWT unit 13 on the basis of the quantization step size. At this time, the quantization step size Δ_(b) of less than 1 is multiplied by powers of 2 to obtain a numerical value of 1 or more for quantization. These operations are similar to those previously described in the first preferred embodiment.

The transform coefficients QD having been quantized in the quantization unit 14 and outputted therefrom are entropy coded on a block-by-block basis in the coefficient bit modeling unit 20 and arithmetic coding unit 21, and are inputted to the rate control unit 22.

ROI Setting

As previously described in the first preferred embodiment, the ROI unit 15 shown in FIG. 18 generates a mask signal indicative of a ROI specified by a user's indication (or based on automatic analysis of image data), and expands the mask signal into a wavelet plane. Then, the ROI unit 15 inputs ROI information on the wavelet plane to the rate control unit 22.

For example, as shown in FIG. 19, let four ROIs 36 to 39 and a non-ROI 40 are specified on image data, and the ROIs 36, 37, 38 and 39 are assigned priorities in this order from highest to lowest. The ROI unit 15 expands these ROIs into a wavelet plane based on the ROI information and priority information, as previously described in the first preferred embodiment.

It should be noted that expansion of the ROIs is performed in each of the code blocks obtained by dividing image data having been subjected to DWT and expanded into a wavelet plane into rectangular regions of predetermined size. More specifically, for example, each subband is divided into code blocks of predetermined size of, e.g., 32×32 pixels, and each code block is judged whether it represents a ROI or not, and when judged as a ROI, priority is assigned, and then expanded into the wavelet plane.

In the case where one code block contains a plurality of ROIs, a ROI assigned the highest priority among those ROIs is selected and allocated to that code block. In the case where one code block contains both a ROI and a non-ROI, the ROI is selected and assigned to that code block. That is, a region assigned higher priority is selected and allocated to each code block.

As a result, following the setting of the ROIs shown in FIG. 19 and priority setting, an expanded wavelet plane as shown in FIG. 20 is obtained. In FIG. 20, each rectangle constituting a subband represents a code block.

The ROI information set in this way is inputted to the rate control unit 22.

Sorting and Bit Shifting

The rate control unit 22 shown in FIG. 18 sorts and bit shifts the inputted coded data AD with the quantization step size given by the image-quality control unit 23. At this time, the transform coefficients quantized with the quantization step size which has been converted into a value of 1 or more are shifted to the left by the number of bits corresponding to the exponent of the powers of 2 used for multiplication to convert the quantization step size.

These operations are similar to those described in the first preferred embodiment, except that they are performed in the rate control unit 22, rather than in the quantization unit 14, and redundant explanation is thus omitted here.

For example, as described in the first preferred embodiment, let the quantization parameter Q_(P)=16 and visual weighting optimized for a viewing distance of 3000 is applied to YUV 422 color image data. Then, such data is sorted and bit shifted with the quantization step sizes, to obtain the code sequence as shown in FIG. 15.

Next, the rate control unit 22 further sorts and bit shifts the code sequence shown in FIG. 15 based on the ROI information given by the ROI unit 15. Allocating higher rate to ROIs than to a non-ROI, degradation in image quality of the ROIs caused by coding processing can be avoided or suppressed.

More specifically, the rate control unit 22 first divides the code sequence shown in FIG. 15 having been sorted and bit shifted with the quantization step size Δ_(b) as previously described, into ROI data and non-ROI data based on the ROI information given by the ROI unit 15.

Although FIG. 15 shows a code sequence for each of the subbands shown in FIG. 5, data in each subband shown in FIG. 15 actually contains all of data on four ROIs 36 a to 39 a and a non-ROI 40 a shown in FIG. 20, as understood from the comparison between FIGS. 5 and 20.

The rate control unit 22 divides the code sequence shown in FIG. 15 into data on the four ROIs 36 to 39 and data on the non-ROI 40 as shown in FIG. 21 based on the ROI information given by the ROI unit 15.

Further, the rate control unit 22 shifts each data on the ROIs 36 to 39 to the left by a predetermined number of bits. In the case where a plurality of ROIs are specified, the amount of shift is set greater for data assigned higher priority.

For example, in the case of FIG. 21, relative to the data on the non-ROI 40, the data on the ROI 39 assigned the lowest priority is shifted by 1 bit, the data on the ROI 38 assigned the second lowest priority by 2 bits, the data on the ROI 37 assigned the second highest priority by 3 bits, and the data on the ROI 36 assigned the highest priority by 4 bits, respectively, to the left.

It should be noted that the amount of shift of each of the data on the ROIs 36 to 39 and data on the non-ROI 40 may be a predetermined number of bits, or alternatively, may be changed to an arbitrary value according to target quality of image data after compression and coding, and the like.

Further, the present embodiment is not limited to a form in which the data on the ROIs 36 to 39 and data on the non-ROI 40 are sorted in descending order of priority as shown in FIG. 21, but may be implemented by another form, for example, in which those data are sorted in ascending order of priority. Data ordering is determined taking into account, for example: the fact that rate control targeted for lower-order bit planes for making the total data capacity meet a target rate is effective in rate control which will be described later since data capacities of lower-order bit planes increase by degradation in coding efficiency; the relationship between the order of data truncation in the rate control at this time and priorities assigned to the ROIs 36 to 39 and non-ROI 40; the contents of image data on the ROIs 36 to 39 and non-ROI 40; and the like.

Rate Control

Next, using the sorted code sequence as shown in FIG. 21, the rate control unit 22 truncates data so that a total capacity of data falls within a predetermined capacity which meets a target rate. The data truncation occurs in sequence from the rightmost bit. For example, in the case of FIG. 21, data is truncated from data of bit 0 in the subband VHL4 in the code sequence for the non-ROI 40 downwardly in sequence through data of bit 0 in the subband YHH5, and so on. If the total capacity of data can be managed within a predetermined capacity by truncation of up to bit data in the subband UHH1, data in those subbands will be truncated. If the total capacity of data cannot be managed within a predetermined capacity by truncation of up to data of bit 0 in the subband YHH1, then, data will be truncated from data of bit 0 in the subband VHL4 in the code sequence for the ROI 39 downwardly in sequence through data of bit 0 in the subband YHH5, and so on. The data truncation is continued in sequence from data of the rightmost bit until the total capacity of data meets the target rate.

The bit data (bit plane) can be divided into the SIG pass, MR pass and CL pass, as shown in FIG. 29. The rate control performed by the rate control unit 22 may be performed on a bit-plane-by-bit-plane basis as previously described in the first preferred embodiment, or alternatively, on a pass-by-pass basis.

It should be noted that if, at a stage of being inputted to the rate control unit 22, the total capacity of the coded data AD is already equal to or smaller than a target rate intended by the user, the aforementioned rate control is not necessary.

In this way, bit data or pass in each subband, sorted and bit shifted with the value of the quantization step size Δ_(b), and further sorted and bit shifted based on the ROI information, is truncated from the lower-order bits, by which rate control is achieved.

As a result, the amount of data can be precisely controlled.

Unlike the conventional method, the rate control process according to this preferred embodiment eliminates the necessity of calculating the amount of distortion in each coding pass for rate-distortion optimization and thereby achieves highly efficient rate control with high immediacy and with significantly reduced overhead.

The data truncation occurs from the code sequence for the non-ROI 40 according to the priories assigned to the ROIs. This can avoid or suppress the information on a ROI assigned higher priority from being lost due to the rate control, achieving compression and coding with the image quality of the ROI assigned higher priority being maintained.

At this time, the influence exerted by the rate control on the image quality of image data after compression and coding, that is, whether or not data is subjected to truncation, can be controlled by freely changing the amount of shift of ROI data relative to non-ROI data.

More specifically, for example, in the case of FIG. 21, the data on the ROI 36 assigned the highest priority will not be subjected to truncation by setting the data on the ROI 36 to be shifted to the left by 15 bits, except for the case in which the total capacity of data cannot be managed within a predetermined capacity by truncation of all the other data on the ROIs 37 to 39 and non-ROI 40. Since the amount of shift can be controlled for each ROI data, the image quality of each ROI after compression and coding can freely be changed.

Further, according to this preferred embodiment, sorting and bit shifting processes are all performed in the rate control unit 22. Accordingly, the present embodiment can also be implemented by a compression encoder not provided with the function achieved by the Max-shift method which is specified as an optional function of JPEG2000, only by changing the function and operation of the rate control unit 22.

Third Preferred Embodiment

According to the present invention as previously described in the first preferred embodiment, an image signal is not always necessarily divided into tiles in the tiling unit 12, and instead a single frame of image signal may be outputted as-is to a functional block subsequent to the tiling unit 12. Accordingly, the first and second preferred embodiments have described performing quantization and rate control without such tiling.

In contrast, this third preferred embodiment is implemented by performing tiling before quantization as well as performing rate control based on ROI information set in each tile. Hereinbelow, the procedure of compression and coding in the case of performing tiling is described, particularly directing to differences from the first and second preferred embodiments.

Compression Encoder

A compression encoder according to the present embodiment has a similar configuration to that of the second preferred embodiment shown in FIG. 18, and redundant explanation is omitted here.

The present embodiment differs from the second preferred embodiment in that: the quantization unit 14 performs quantization based on the result of tiling performed in the tiling unit 12; the ROI unit 15 assigns priority to each ROI using the result of tiling; and the rate control unit 22 performs rate control based on the ROI information according to assigned priorities.

The quantization unit 14 quantizes transform coefficients inputted from the DWT unit 13 with the quantization step size determined by the image-quality control unit 23 based on target image-quality information which is provided from the outside, as previously described in the second preferred embodiment.

The ROI unit 15 performs ROI setting as previously described in the first preferred embodiment; however, ROI setting including priority setting is performed in each tile.

The transform coefficients QD outputted from the quantization unit 14 are entropy coded on a block-by-block basis in the coefficient bit modeling unit 20 and the arithmetic coding unit 21, and they are rate controlled in the rate control unit 22.

At this time, according to the first preferred embodiment, the quantization unit 14 performs sorting and bit shifting with the quantization step size Δ_(b) and sorting and bit shifting based on the ROI information given by the ROI unit 15; however, according to this third preferred embodiment, the rate control unit 22 performs these operations as preliminary processing prior to the rate control.

The respective components of the compression encoder 50 function and operate similarly to the compression encoder according to the first preferred embodiment, and redundant explanation is thus omitted here.

Quantization

The image-quality control unit 23 determines the quantization step size on the basis of target image-quality information which is provided from the outside, and the quantization unit 14 quantizes the transform coefficients inputted from the DWT unit 13 with the quantization step size. These operations are performed for each tile.

For example, if YUV color image data is divided into tiles T1 to Tn for processing as shown in FIG. 22, the quantization step size Δ_(b) is obtained for every one of the luminance signal Y and the color difference signals U and V in each tile. At this time, the quantization step size Δ_(b) of less than 1 is multiplied by powers of 2 to obtain a numerical value of 1 or more for quantization. Processing is performed in a similar way to the first preferred embodiment, except that it is performed in each tile.

The transform coefficients QD quantized in the quantization unit 14 and outputted therefrom are entropy coded on a block-by-block basis in the coefficient bit modeling unit 20 and arithmetic coding unit 21, and are inputted to the rate control unit 22.

ROI Setting

As previously described in the first preferred embodiment, the ROI unit 15 generates a mask signal indicative of a ROI specified by a user's indication (or based on automatic analysis of image data), and expands the mask signal into a wavelet plane, and inputs the ROI information on the wavelet plane to the rate control unit 22.

However, ROI setting is performed in each of tiles obtained by dividing image data into rectangular regions of predetermined size. More specifically, in the case of FIG. 22 in which image data is tiled, each of the tiles T1 to Tn is given one of priorities Ta to Tz. The priorities decrease in this order, that is, the relation: Ta>Tb> . . . >Tz holds. In other words, the lowest priority Tz corresponds to a non-ROI.

In the case where one tile contains a plurality of ROIs, a ROI assigned the highest priority among those ROIs is selected and allocated to that tile. In the case where one tile contains both a ROI and a non-ROI, the ROI is selected and assigned to that tile. That is, a region assigned higher priority is selected and allocated to each tile.

ROI information set in this way is inputted to the rate control unit 22.

Sorting and Bit Shifting

The rate control unit 22 sorts and bit shifts the inputted coded data AD based on the quantization step size given by the image-quality control unit 23. At this time, the transform coefficients quantized with a quantization step size converted into a value of 1 or more are shifted to the left by the number of bits that corresponds to the exponent of the powers of 2 used for multiplication to convert the quantization step size.

When processing a tiled color image, there are data on the luminance signal Y and the color difference signals U and V in each tile; however, all those data are sorted together for bit shifting in ascending order of quantization step size without being classified by tile or signal.

These operations are similar to those described in the first preferred embodiment except that each tile contains data, and redundant explanation is thus omitted here.

For example, if, as described in the first preferred embodiment, the quantization parameter Q_(P)=16 and YUV 422 color image data is tiled as shown in FIG. 22, quantized by visual weighting optimized for a viewing distance of 3000, and sorted and bit shifted with the quantization step size, the code sequence as shown in FIG. 23 is obtained. In the figure, YT1LL5 represents data on the luminance signal Y in the subband LL5 in the tile T1. In this way, all the data on the luminance signal Y and the color difference signals U and V in all of the tiles T1 to Tn are subjected to bit shifting and sorting processes on the basis of the quantization step size, as previously described in the first preferred embodiment.

Next, the rate control unit 22 further performs sorting and bit shifting on the code sequence shown in FIG. 23 based on the ROI information given by the ROI unit 15. Allocating higher rate to a ROI assigned higher priority, degradation in image quality of ROI assigned higher priority caused by coding processing can be avoided or suppressed.

More specifically, the rate control unit 22 first classifies data in the code sequence shown in FIG. 23 into data according to priority based on the ROI information given by the ROI unit 15. More specifically, the code sequence shown in FIG. 23 is classified into data according to the priorities Ta to Tz assigned to the respective tiles based on the ROI information given by the ROI unit 15, and is sorted as shown in FIG. 24.

For example, let the tiles T1 and T2 are assigned the priority Ta, the tiles T3 and T4 the priority Tb, and the tiles T5 and T6 the priority Tz. Then, as shown in FIG. 24, data assigned the priority Ta shall contain data on each of the YUV signals in every subband in the tiles T1 and T2. Similarly, data assigned the priority Tb shall contain data on each of the YUV signals in every subband in the tiles T3 and T4, and data assigned the priority Tz shall contain data on each of the YUV signals in every subband in the tiles T5 and T6. All the data are sorted in descending order of priority assigned to each tile, and in tiles assigned the same priority, data are sorted in ascending order of quantization step size.

Next, the rate control unit 22 shifts each data to the left by a predetermined number of bits according to the priorities Ta to Tz. The higher the priority, the greater the amount of shift. For example, in the case of FIG. 24, data assigned the priority Tb is shifted by 2 bits, and data assigned the priority Ta is shifted by 4 bits, respectively, to the left, relative to the non-ROI data assigned the priority Tz.

It should be noted that the amount of shift of each data assigned priorities may be a predetermined number of bits, or alternatively, may be changed to an arbitrary value according to target image quality of image data after compression and coding, and the like.

Further, the present embodiment is not limited to a form in which the data are sorted in descending order of priorities Ta, Tb, . . . , as shown in FIG. 24, but may be implemented by another form, for example, in which the data are sorted in ascending order of priority. Data ordering is determined taking into account, for example: the fact that rate control targeted for lower-order bit planes for making the total data capacity meet a target rate is effective in the rate control which will be described later since data capacities of lower-order bit planes increase by degradation in coding efficiency; the relationship between the order of data truncation in the rate control at this time and priorities Ta, Tb, . . . ; the contents of image data assigned the priorities Ta, Tb, . . . , respectively; and the like.

Rate Control

Next, using the sorted code sequence as shown in FIG. 24, the rate control unit 22 truncates data so that a total capacity of data falls within a predetermined capacity which meets a target rate. The data truncation occurs in sequence from the rightmost bit. For example, in the case of FIG. 24, data is truncated from data of bit 0 in the subband VT5HL4 in the code sequence for the non-ROI assigned the priority Tz downwardly in sequence through data of bit 0 in the subband VT6HL4, and so on. If the total capacity of data can be managed within a predetermined capacity by truncation of up to bit data in the subband YTnHH1, data in those subbands will be truncated. If the total capacity of data cannot be managed within a predetermined capacity by truncation of up to bit data in the subband YTnHH1, then, data will be truncated from data of bit 0 in the subband VT5LL4 downwardly in sequence through data of bit 0 in the subband VT6LL4, and so on.

The bit data (bit plane) can be divided into the SIG pass, MR pass and CL pass, as shown in FIG. 29. The rate control performed by the rate control unit 22 may be performed on a bit-plane-by-bit-plane basis as described above in the first preferred embodiment, or alternatively, on a pass-by-pass basis.

It should be noted that if, at a stage of being inputted to the rate control unit 22, the total capacity of the coded data AD is already equal to or smaller than a target rate intended by the user, the aforementioned rate control is not necessary. In this way, bit data, sorted and bit shifted by the value of the quantization step size Δ_(b), and further sorted and bit shifted based on the ROI information, is truncated from the lower-order bits, by which rate control is achieved.

As a result, the amount of data can be precisely controlled.

Unlike the conventional method, the rate control process according to this preferred embodiment eliminates the necessity of calculating the amount of distortion in each coding pass for rate-distortion optimization and thereby achieves highly efficient rate control with high immediacy and with significantly reduced overhead.

The data truncation occurs according to the priorities assigned to ROIs. This can avoid or suppress information on a ROI assigned higher priority from being lost due to the rate control.

At this time, the influence exerted by the rate control on the image quality of image data after compression and coding, that is, whether or not data is subjected to truncation, can be controlled by freely changing the amount of shift of ROI data relative to non-ROI data.

More specifically, for example, in the case of FIG. 24, the ROI data assigned the highest priority Ta will not be subjected to truncation by setting that ROI data to be shifted to the left by 15 bits, except for the case in which the total capacity of data cannot be managed within a predetermined capacity by truncation of all the other ROI data respectively assigned the priorities Tb to Ty and non-ROI data assigned the priority Tz. Since the amount of shift can be controlled for each ROI data, the image quality of each ROI after compression and coding can freely be changed.

Further, according to this preferred embodiment, sorting and bit shifting processes are all performed in the rate control unit 22. Accordingly, the present embodiment can also be implemented by a compression encoder not provided with the function achieved by the Max-shift method which is specified as an optional function of JPEG2000, only by changing the function and operation of the rate control unit 22.

While the invention has been shown and described in detail, the foregoing description is in all aspects illustrative and not restrictive. It is therefore understood that numerous modifications and variations can be devised without departing from the scope of the invention. 

1. A compression encoder for compression and coding of an image signal, comprising: a wavelet transformer that recursively divides an image signal into high- and low-pass components by wavelet transform and generates and outputs transform coefficients in a plurality of bandpass components; an image-quality controller that determines a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient; a region-of-interest setting unit that sets at least one region of interest in said image signal; a quantizer that quantizes and sorts said transform coefficients with said quantization step size as well as sorts and bit shifts said transform coefficients on the basis of information on setting said at least one region of interest; an entropy coder that entropy codes said transform coefficients inputted from said quantizer; and a rate controller that controls a rate by truncating part of coded data outputted from said entropy coder so that a total capacity of said coded data meets a target rate.
 2. The compression encoder according to claim 1, wherein said rate controller determines a target to be truncated in said rate control on a bit-plane-by-bit-plane basis.
 3. The compression encoder according to claim 1, wherein said rate controller determines a target to be truncated in said rate control on a pass-by-pass basis.
 4. The compression encoder according to claim 1, wherein said image-quality controller determines said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 5. The compression encoder according to claim 1, wherein said image-quality controller, when said quantization step size is less than a predetermined value, multiplies said quantization step size by powers of 2 so that said quantization step size is not less than said predetermined value.
 6. The compression encoder according to claim 5, wherein said rate controller, when said quantization step size is a value obtained by multiplication by powers of 2 in said image-quality controller, bit shifts said transform coefficients, by the number of bits corresponding to an exponent of said powers of 2 in performing sorting with said quantization step size.
 7. A compression encoder for compression and coding of an image signal, comprising: a wavelet transformer that recursively divides an image signal into high- and low-pass components by wavelet transform and generates and outputs transform coefficients in a plurality of bandpass components; a region-of-interest setting unit that sets at least one region of interest in said image signal; a quantizer that quantizes said transform coefficients; an entropy coder that entropy codes said transform coefficients inputted from said quantizer; and a rate controller that controls a rate by sorting and bit shifting coded data outputted from said entropy coder on the basis of information on setting said at least one region of interest to generate a code sequence and then by truncating part of said code sequence so that a total capacity of said coded data meets a target rate.
 8. The compression encoder according to claim 7, further comprising an image-quality controller that determines a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient, wherein said quantizer quantizes said transform coefficients with said quantization step size, and said rate controller sorts said coded data with said quantization step size.
 9. The compression encoder according to claim 7, wherein said at least one region of interest includes a plurality of regions of interest, said region-of-interest setting unit assigns a priority to each of said plurality of regions of interest specified, and the amount of shift in said bit shifting on the basis of said information on setting said plurality of regions of interest is determined by said priority.
 10. The compression encoder according to claim 7, wherein said rate controller determines a target to be truncated in said rate control on a bit-plane-by-bit-plane basis.
 11. The compression encoder according to claim 7, wherein said rate controller determines a target to be truncated in said rate control on a pass-by-pass basis.
 12. The compression encoder according to claim 8, wherein said image-quality controller determines said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 13. A compression encoder for compression and coding of an image signal, comprising: a tiling unit that divides an image signal into tiles; a wavelet transformer that recursively divides said image signal divided into tiles into high- and low-pass components by wavelet transform, and generates and outputs transform coefficients in a plurality of bandpass components; a region-of-interest setting unit that sets at least one region of interest in each of said tiles in said image signal; a quantizer that quantizes said transform coefficients; an entropy coder that entropy codes said transform coefficients inputted from said quantizer; and a rate controller that controls a rate by sorting and bit shifting coded data outputted from said entropy coder on the basis of information on setting said at least one region of interest to generate a code sequence and then by truncating part of said code sequence so that a total capacity of said coded data meets a target rate.
 14. The compression encoder according to claim 13, further comprising an image-quality controller that determines a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient, wherein said quantizer quantizes said transform coefficients with said quantization step size, and said rate controller sorts said coded data with said quantization step size.
 15. The compression encoder according to claim 13, wherein said at least one region of interest includes a plurality of regions of interest, said region-of-interest setting unit assigns a priority to each of said plurality of regions of interest specified, and the amount of shift in said bit shifting on the basis of said information on setting said plurality of regions of interest is determined by said priority.
 16. The compression encoder according to claim 13, wherein said rate controller determines a target to be truncated in said rate control on a bit-plane-by-bit-plane basis.
 17. The compression encoder according to claim 13, wherein said rate controller determines a target to be truncated in said rate control on a pass-by-pass basis.
 18. The compression encoder according to claim 14, wherein said image-quality controller determines said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 19. A compression encoding method, implemented on a compression encoder, for compression and coding of an image signal, comprising the steps of: recursively dividing, at the compression encoder, an image signal into high- and low-pass components by wavelet transform and generating and outputting transform coefficients in a plurality of bandpass components; determining, at the compression encoder, a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient; setting, at the compression encoder, at least one region of interest in said image signal; quantizing and sorting, at the compression encoder, said transform coefficients with said quantization step size as well as sorting and bit shifting said transform coefficients on the basis of information on setting said at least one region of interest; entropy coding, at the compression encoder, said transform coefficients subjected to said quantizing and sorting; and controlling, at the compression encoder, a rate by truncating part of coded data obtained in said entropy coding so that a total capacity of said coded data meets a target rate.
 20. The compression encoding method according to claim 19, wherein said controlling includes the step of determining a target to be truncated on a bit-plane-by-bit-plane basis.
 21. The compression encoding method according to claim 19, wherein said controlling includes the step of determining a target to be truncated on a pass-by-pass basis.
 22. The compression encoding method according to claim 19, wherein said determining includes the step of determining said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 23. The compression encoding method according to claim 19, wherein said determining includes the step of, when said quantization step size is less than a predetermined value, multiplying said quantization step size by powers of 2 so that said quantization step size is not less than said predetermined value.
 24. The compression encoding method according to claim 23, wherein said quantizing includes the step of, when said quantization step size is a value obtained by multiplication by powers of 2 in said image-quality controller, bit shifting said transform coefficients which has been quantized with said quantization step size, by the number of bits corresponding to an exponent of said powers of
 2. 25. A compression encoding method, implemented on a compression encoder, for compression and coding of an image signal, comprising the steps of: recursively dividing, at the compression encoder, an image signal into high- and low-pass components by wavelet transform and generating and outputting transform coefficients in a plurality of bandpass components; setting, at the compression encoder, at least one region of interest in said image signal; quantizing, at the compression encoder, said transform coefficients; entropy coding, at the compression encoder, said transform coefficients obtained in said quantizing; and controlling, at the compression encoder, a rate by sorting and bit shifting coded data obtained in said entropy coding on the basis of information on setting said at least one region of interest to generate a code sequence and then by truncating part of said code sequence so that a total capacity of said coded data meets a target rate.
 26. The compression encoding method according to claim 25, wherein said quantizing includes the steps of: determining a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient; and quantizing said transform coefficients with said quantization step size, and said controlling includes the step of sorting said coded data with said quantization step size.
 27. The compression encoding method according to claim 25, wherein said at least one region of interest includes a plurality of regions of interest, said setting includes the step of assigning a priority to each of said plurality of regions of interest, and said controlling includes the step of bit shifting said coded data by a predetermined number of bits determined by said priority.
 28. The compression encoding method according to claim 25, wherein said controlling includes the step of determining a target to be truncated on a bit-plane-by-bit-plane basis.
 29. The compression encoding method according to claim 25, wherein said controlling includes the step of determining a target to be truncated on a pass-by-pass basis.
 30. The compression encoding method according to claim 26, wherein said determining includes the step of determining said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 31. A compression encoding method, implemented on a compression encoder, for compression and coding of an image signal, comprising the steps of: dividing, at the compression encoder, an image signal into tiles; recursively dividing, at the compression encoder, said image signal divided into said tiles into high- and low-pass components by wavelet transform and generating and outputting transform coefficients in a plurality of bandpass components; setting, at the compression encoder, at least one region of interest in each of said tiles in said image signal; quantizing, at the compression encoder, said transform coefficients; entropy coding, at the compression encoder, said transform coefficients obtained in said quantizing; and controlling, at the compression encoder, a rate by sorting and bit shifting coded data obtained in said entropy coding on the basis of information on setting said at least one region of interest to generate a code sequence and then by truncating part of said code sequence so that a total capacity of said coded data meets a target rate.
 32. The compression encoding method according to claim 31, wherein said quantizing includes the steps of: determining a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient; and quantizing said transform coefficients with said quantization step size, and said controlling includes the step of sorting said coded data with said quantization step size.
 33. The compression encoding method according to claim 31, wherein said at least one region of interest includes a plurality of regions of interest, said setting includes the step of assigning a priority to each of said plurality of regions of interest, and said controlling includes the step of bit shifting said coded data by a predetermined number of bits determined by said priority.
 34. The compression encoding method according to claim 31, wherein said controlling includes the step of determining a target to be truncated on a bit-plane-by-bit-plane basis.
 35. The compression encoding method according to claim 31, wherein said controlling includes the step of determining a target to be truncated on a pass-by-pass basis.
 36. The compression encoding method according to claim 32, wherein said determining includes the step of determining said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 37. A computer readable storage medium encoded with computer executable instructions for compression and coding of an image signal, which when executed by a microprocessor, cause the microprocessor to function as: a wavelet transformer that recursively divides an image signal into high- and low-pass components by wavelet transform and generates and outputs transform coefficients in a plurality of bandpass components; an image-quality controller that determines a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient; a region-of-interest setting unit that sets at least one region of interest in said image signal; a quantizer that quantizes and sorts said transform coefficients with said quantization step size as well as sorts and bit shifts said transform coefficients on the basis of information on setting said at least one region of interest; an entropy coder that entropy codes said transform coefficients inputted from said quantizer; and a rate controller that controls a rate by truncating part of coded data outputted from said entropy coder so that a total capacity of said coded data meets a target rate.
 38. The computer readable storage medium according to claim 37, wherein said microprocessor, when functioning as said rate controller, acts as a unit that determines a target to be truncated on a bit-plane-by-bit-plane basis.
 39. The computer readable storage medium according to claim 37, wherein said microprocessor, when functioning as said rate controller, acts as a unit that determines a target to be truncated on a pass-by-pass basis.
 40. The computer readable storage medium according to claim 37, wherein said microprocessor, when functioning as said image-quality controller, acts as a unit that determines said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 41. The computer readable storage medium according to claim 37, wherein said microprocessor, when functioning as said image-quality controller, acts as a unit that, when said quantization step size is less than a predetermined value, multiplies said quantization step size by powers of 2 so that said quantization step size is not less than said predetermined value.
 42. The computer readable storage medium according to claim 41, wherein said microprocessor, when functioning as said rate controller and sorting said transform coefficients with said quantization step size, acts as a unit that, when said quantization step size is a value obtained by multiplication by powers of 2 in said image-quality controller, bit shifts said transform coefficients by the number of bits corresponding to an exponent of said powers of
 2. 43. A computer readable storage medium encoded with computer executable instructions for compression and coding of an image signal, which when executed by a microprocessor, cause the microprocessor to function as: a wavelet transformer that recursively divides an image signal into high- and low-pass components by wavelet transform and generates and outputs transform coefficients in a plurality of bandpass components; a region-of-interest setting unit that sets at least one region of interest in said image signal; a quantizer that quantizes said transform coefficients; an entropy coder that entropy codes said transform coefficients inputted from said quantizer; and a rate controller that controls a rate by sorting and bit shifting coded data outputted from said entropy coder on the basis of information on setting said at least one region of interest to generate a code sequence and then by truncating part of said code sequence so that a total capacity of said coded data meets a target rate.
 44. The computer readable storage medium according to claim 43, wherein said microprocessor further functions as an image-quality controller that determines a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient, said microprocessor, when functioning as said quantizer, acts as a unit that quantizes said transform coefficients with said quantization step size, and said microprocessor, when functioning as said rate controller, acts as a unit that sorts said coded data with said quantization step size.
 45. The computer readable storage medium according to claim 43, wherein said at least one region of interest includes a plurality of regions of interest, said microprocessor, when functioning as said region-of-interest setting unit, acts as a unit that assigns a priority to each of said plurality of regions of interest, and said microprocessor, when functioning as said rate controller and performing bit shifting on the basis of information on setting said plurality of regions of interest, acts as a unit that bit shifts said coded data by the number of bits determined by said priority.
 46. The computer readable storage medium according to claim 43, wherein said microprocessor, when functioning as said rate controller, acts as a unit that determines a target to be truncated on a bit-plane-by-bit-plane basis.
 47. The computer readable storage medium according to claim 43, wherein said microprocessor, when functioning as said rate controller, acts as a unit that determines a target to be truncated on a pass-by-pass basis.
 48. The computer readable storage medium according to claim 44, wherein said microprocessor, when functioning as said image-quality controller, acts as a unit that determines said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics.
 49. A computer readable storage medium encoded with computer executable instructions for compression and coding of an image signal, which when executed by a microprocessor, cause the microprocessor to function as: a tiling unit that divides an image signal into tiles; a wavelet transformer that recursively divides said image signal divided into tiles into high- and low-pass components by wavelet transform and generates and outputs transform coefficients in a plurality of bandpass components; a region-of-interest setting unit that sets at least one region of interest in each of said tiles in said image signal; a quantizer that quantizes said transform coefficients; an entropy coder that entropy codes said transform coefficients inputted from said quantizer; and a rate controller that controls a rate by sorting and bit shifting coded data outputted from said entropy coder on the basis of information on setting said at least one region of interest to generate a code sequence and then by truncating part of said code sequence so that a total capacity of said coded data meets a target rate.
 50. The computer readable storage medium according to claim 49, wherein said microprocessor further functions as an image-quality controller that determines a quantization step size by dividing a quantization parameter which indicates target image quality by a norm of a synthesis filter coefficient, said microprocessor, when functioning as said quantizer, acts as a unit that quantizes said transform coefficients with said quantization step size, and said microprocessor, when functioning as said rate controller, acts as a unit that sorts said coded data with said quantization step size.
 51. The computer readable storage medium according to claim 49, wherein said at least one region of interest includes a plurality of regions of interest, said microprocessor, when functioning as said region-of-interest setting unit, acts as a unit that assigns a priority to each of said plurality of regions of interest, and said microprocessor, when functioning as said rate controller and performing bit shifting on the basis of information on setting said plurality of regions of interest, acts as a unit that bit shifts said coded data by the number of bits determined by said priority.
 52. The computer readable storage medium according to claim 49, wherein said microprocessor, when functioning as said rate controller, acts as a unit that determines a target to be truncated on a bit-plane-by-bit-plane basis.
 53. The computer readable storage medium according to claim 49, wherein said microprocessor, when functioning as said rate controller, acts as a unit that determines a target to be truncated on a pass-by-pass basis.
 54. The computer readable storage medium according to claim 50, wherein said microprocessor, when functioning as said image-quality controller, acts as a unit that determines said quantization step size applying weighting in consideration of human visual characteristics by dividing said quantization parameter by a value obtained by multiplying a norm of a synthesis filter coefficient by an energy weighting factor which is a predetermined value determined based on the human visual characteristics. 